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[Перевод] LLM-судья: как LLM отсекает правду от лжи?

LLM-as-a-judge — распространённая техника оценки продуктов на основе LLM.

Популярность этой техники обусловлена практичностью: она представляет собой удобную альтернативу дорогостоящей человеческой оценке при анализе открытых текстовых ответов.

Оценивать сгенерированные тексты сложно, будь то «простой» саммари или диалог с чат-ботом. Метрики типа accuracy плохо работают, поскольку «правильный» ответ может быть сформулирован множеством способов, не обязательно совпадающих с образцом. Кроме того, стиль или тон — субъективные характеристики, которые сложно формализовать.

Люди способны учитывать такие нюансы, но ручная проверка каждого ответа плохо масштабируется. В качестве альтернативы появилась техника LLM-as-a-judge: для оценки сгенерированных текстов используются сами LLM. Интересно, что LLM одновременно являются и источником проблемы, и её решением!

В этой статье мы рассмотрим:

Как работает LLM-as-a-judge и почему этот метод эффективен.
Типы LLM-судей для офлайн- и онлайн-оценки.
Как создать оценщик на основе LLM и грамотно составить промпт.
Плюсы, минусы и альтернативы использования LLM для оценки.

Хотя существует много качественных исследований на эту тему (мы приведем некоторые из них), данное руководство носит прежде всего практический характер. Оно предназначено для всех, кто занимается разработкой продуктов на основе LLM и размышляет, подойдет ли им этот подход.

Кратко

LLM-as-a-judge — метод оценки качества текстовых ответов продуктов на основе LLM, таких как чат-боты, Q&A-системы или агенты.
Метод подразумевает использование большой языковой модели (LLM) с оценочным промптом для выставления оценок сгенерированным текстам на основе заданных вами критериев.
Судьи на основе LLM могут осуществлять как попарные сравнения (pairwise comparisons), так и прямую оценку (direct scoring) свойств текста, таких как корректность или релевантность.
Это не отдельная метрика, а гибкий метод приближенной имитации человеческих суждений. Оценки LLM-судей специфичны для конкретного приложения.
Успешность применения метода зависит от качества промпта, выбранной модели и сложности задачи.
LLM-судьи могут использоваться как в офлайн-, так и в онлайн-оценке.
Плюсы подхода: гибкость, экономичность, скорость и доступность экспертизы в различных предметных областях.

Что такое LLM-as-a-judge?

Коротко: LLM-as-a-Judge — это подход, при котором LLM используются для оценки текстов, сгенерированных ИИ, на основе пользовательских критериев, заданных в оценочном промпте.

Разрабатывая продукт на основе LLM, будь то чат-бот, генератор кода или ассистент для электронной почты, вам необходимо оценивать качество его работы.

Во время разработки: для сравнения различных моделей или промптов и проверки улучшений.
После запуска: для мониторинга качества и безопасности взаимодействий с пользователями.
При любых изменениях: для проведения регрессионного тестирования и предотвращения возможных ошибок.

LLM-as-a-judge — это метод оценки, который подходит для всех этих сценариев. Его идея проста: попросите LLM «оценить» текстовые ответы, используя критерии, которые вы определяете сами.

Допустим, у вас есть чат-бот. Вы можете использовать внешнюю LLM для оценки его ответов так же, как это сделал бы человек, обращая внимание на такие характеристики, как:

Вежливость: является ли ответ уважительным и тактичным?
Предвзятость (Bias): проявляется ли в ответе предубеждение к определенной группе?
Тон: является ли тон формальным, дружелюбным или разговорным?
Сентимент (Sentiment): положительные, отрицательные или нейтральные эмоции выражены в тексте?
Галлюцинации: соответствует ли ответ заданному контексту?

Для применения метода вы берёте текстовый ответ от своей системы и подаете его в LLM повторно, но уже вместе с оценочным промптом. В результате LLM вернёт оценку, метку или даже описательный комментарий согласно вашим инструкциям.

Главное преимущество подхода — автоматическая оценка текстовых ответов с учетом индивидуальных критериев, специфичных именно для вашей задачи.

Например, вы можете указать LLM оценивать, насколько полезны ответы вашего чат-бота. (Примечание: все промпты в этом руководстве приводятся только для иллюстрации).

Упрощённый промпт: «Даны ВОПРОС и ОТВЕТ. Оцени, полезен ли данный ответ. Полезные ответы ясные, релевантные и дают возможность действовать. Неполезные ответы расплывчаты, не по теме или недостаточно детализированы. Верни метку: 'полезный' или 'неполезный'.»

Если вы запустите его на ответах вашего чат-бота, LLM-судья выставит оценку каждому ответу.

Затем вы можете обобщить результаты по всему датасету диалогов, чтобы увидеть распределение: какова доля полезных ответов?

Речь идёт не только о разовом анализе: такие оценки можно проводить непрерывно на реальных данных. Это позволит отслеживать, насколько хорошо работает чат-бот, и оперативно выявлять проблемы, например, резкий рост «неполезных» ответов.

Хотите оценить другие свойства ответов? Просто напишите новый промпт!

Стоит отметить, что LLM-as-a-judge — это не метрика оценки в том же смысле, что accuracy, precision или NDCG. В машинном обучении метрика — это четко определенная объективная мера: она точно количественно выражает, насколько предсказания модели соответствуют истинным данным (ground truth).

Напротив, LLM-as-a-judge — это общий подход, при котором вы используете LLM для приближенной имитации человеческой оценки. Когда вы просите LLM оценить такие качества, как «соответствие исходному тексту» (faithfulness to source), «корректность» (correctness) или «полезность» (helpfulness), вы сами определяете значения этих терминов в оценочном промпте, полагаясь на семантические взаимосвязи, усвоенные моделью из обучающих данных.

LLM следует вашим инструкциям так же, как это делал бы человек. Затем вы можете отслеживать, сколько ответов было помечено как «хорошие», но это не фиксированная детерминированная мера, а специфичная для конкретного кейса прокси-метрика.

Успех применения LLM-судей также сильно зависит от деталей реализации — выбранной модели, дизайна промпта и сложности задачи. Кроме того, оценочный промпт необходимо адаптировать к конкретной используемой модели LLM: имеют значение и слова, и формат.

Прежде чем перейти к деталям, давайте обсудим очевидный вопрос: как так получилось, что вы используете LLM для оценки работы «самой себя»? Разве это не читерство?

Как это работает?

Коротко: оценивать свойства текста проще, чем его генерировать. Оценочная LLM является внешней по отношению к основному продукту и решает более простую, четко сформулированную задачу.

На первый взгляд может показаться странным использовать LLM для оценки текстовых ответов. Если LLM генерирует ответы, почему она должна лучше судить о них или замечать ошибки?

Суть в том, что мы не просим LLM переделать её собственную работу. Вместо этого мы используем другой промпт — или даже другую LLM — для выполнения отдельной задачи: оценки конкретных свойств текста.

Делая внешний вызов LLM с четко сфокусированным оценочным промптом, вы задействуете другие возможности модели. Часто модель используется в роли простого классификатора текста, который должен выполнить одну конкретную инструкцию.

Можно представить это следующим образом: критиковать проще, чем создавать. Классифицировать контент проще, чем его генерировать. Заметить, что что-то пошло не так, обычно легче, чем изначально предотвратить ошибку.

При генерации ответов LLM работает с множеством переменных, интегрирует сложный контекст и пользовательские вводные данные, следуя при этом детальным product промптам, которые могут содержать сразу несколько инструкций. Такая сложность и приводит к ошибкам.

Оценка ответов обычно более проста и прямолинейна. Мы не просим LLM исправлять или корректировать себя, а просто оцениваем то, что уже было создано. Например, при оценке релевантности модели достаточно проверить, является ли ответ семантически релевантным вопросу, а не генерировать лучший вариант ответа.

Аналогично, если ваш чат-бот был обманут злоумышленником и сгенерировал, например, предвзятый контент, внешняя LLM-оценочная модель всё равно способна это обнаружить. Такая модель-оценщик работает независимо от самого диалога: она изучает сгенерированный текст и оценивает его по заданным критериям. Например: «Содержит ли этот текст предвзятость по отношению к какой-либо группе? Да или нет». Поскольку LLM обучаются на огромных объемах текстовых данных, они хорошо умеют распознавать языковые паттерны.

Это не означает, что оценочные LLM «лучше» вашей основной модели. Просто перед ними стоит более простая и чётко сфокусированная задача.

Давайте рассмотрим несколько типов LLM-судей, которые вы можете реализовать.

Типы LLM-судей

Коротко: оценка свойств текста проще, чем его генерация. Оценщик на основе LLM является внешним по отношению к основному продукту и решает более простую и четко определенную задачу.

Вы можете использовать LLM-оценщики в нескольких сценариях:

Парное сравнение: передайте LLM два ответа и попросите выбрать лучший. Это позволяет сравнивать модели, промпты или конфигурации, чтобы определить, какая из них показывает наилучшие результаты.
Оценка по критериям (без эталона): запросите у LLM оценку ответа или диалога на основе таких параметров, как тон, ясность, корректность и другие аспекты.
Оценка по критериям (с эталоном): предоставьте дополнительный контекст, например исходный документ или референс, и попросите LLM выставить оценку ответу.

Парное сравнение обычно выполняется офлайн: вам необходимо сгенерировать несколько ответов и затем их сравнить. Прямая оценка (scoring) работает как офлайн, так и онлайн для непрерывного мониторинга.

Парное сравнение

Одним из первых применений техники LLM-as-a-judge стало парное сравнение, при котором LLM анализирует два ответа и определяет, какой из них лучше. Этот метод был описан, например, в этом блоге, а также получил официальное название "judge" в статье “Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena” (Zheng et al., 2023). MT-Bench и Chatbot Arena являются примерами бенчмарков для оценки LLM.

Вкратце, метод заключается в генерации двух ответов и последующем выборе LLM наиболее подходящего из них на основе заданных критериев.

В упомянутом исследовании авторы использовали GPT-4 в качестве оценочной модели и сравнили его решения с краудсорсинговыми оценками людей. Совпадение оценок LLM и человеческих аннотаторов превысило 80%, что сопоставимо с уровнем согласованности между самими экспертами.

Этот результат говорит о высокой эффективности метода, и он уже стал частью практических рабочих процессов. Он особенно полезен на этапе разработки, когда требуется выбрать лучшую модель или промпт, запустив серию парных сравнений.

Упрощённый промпт: "Вам будут показаны два ответа на один и тот же вопрос. Ваша задача — определить, какой из них лучше по релевантности, полезности и уровню детализации. Если оба ответа одинаково хороши, объявите ничью."

Пример из статьи “Judging LLM-as-a-Judge”.

Способность LLM совпадать с человеческими предпочтениями при сравнении двух ответов выглядит многообещающе. Однако более распространённый способ использования LLM-оценщиков среди разработчиков LLM-продуктов ещё проще — это прямое оценивание (direct scoring) отдельных ответов.

Оценка по критериям

Вы можете попросить LLM напрямую оценивать сгенерированные тексты по любым заданным вами параметрам. Вместо фокусировки на общей «предпочтительности» модель может анализировать каждый критерий отдельно. Это особенно полезно для мониторинга в продакшене.

Например, LLM-оценщики могут проверять такие аспекты, как тон, ясность, соответствие формату, лаконичность, вежливость, наличие персонально идентифицируемой информации (PII) и другие параметры.

Эти примеры носят общий характер. В вашем конкретном приложении, скорее всего, потребуется разработка кастомных оценочных метрик, которые отражают, что именно считается качественным ответом, или какие ошибки вы хотите отлавливать.

LLM-модели эффективны в задачах классификации, связанных с языком и семантикой. Оценку можно организовать как бинарную классификацию (например, «соответствует» / «не соответствует») либо использовать рейтинговую шкалу – например, шкалу Лайкерта от 1 до 5.

Упрощённый промпт: "Оцените следующий текст по критерию лаконичности. Лаконичный ответ чётко и прямо передаёт основную мысль без лишних слов. Верните один из следующих ярлыков: 'Concise' (лаконичный) или 'Verbose' (размытый, избыточный)."

Использование LLM для автоматической оценки рассматривается в следующих работах:

“Can Large Language Models Be an Alternative to Human Evaluation?” (Chiang et al., 2023) – сравнивает оценки LLM с человеческими аннотациями при использовании одинаковых инструкций.
“GPTScore: Evaluate as You Desire” (Fu et al., 2023) – анализирует 22 различных аспектов оценки.
“Is ChatGPT a Good NLG Evaluator? A Preliminary Study” (Wang et al., 2023) – исследует оценку LLM в разрезе конкретных задач и аспектов.

Также возможно оценивать целые диалоги. В этом случае передаётся полный многотуровый транскрипт, включающий все вопросы пользователя в рамках одной сессии и ответы LLM.

Пока транскрипт диалога укладывается в контекстное окно LLM (объем текста, который модель может обработать за раз, обычно несколько тысяч токенов), модель способна с этим справиться. Это остаётся задачей оценивания (grading), только с более длинным входным текстом.

Примеры оценки диалогов:

Обнаружение отказов: Отказывался ли агент выполнить задачу в какой-либо момент?
Выявление повторов: Приходилось ли пользователю повторять свой запрос?
Определение пользовательского раздражения: Проявляет ли пользователь негативные эмоции?
Проверка решения проблемы: Была ли проблема пользователя решена к концу диалога?

Эти оценки можно использовать для мониторинга трендов и маркировки отдельных диалогов для последующего анализа человеком.

Упрощённый промпт: "Прочитайте диалог и оцените, был ли запрос пользователя решён. 'Resolved' означает, что проблема была устранена, и пользователь либо подтвердил это, либо выразил удовлетворение. Верните один из следующих ярлыков: 'Resolved' (решено) или 'Not Resolved' (не решено)."

Оценка на основе эталона

В оценке на основе эталона LLM анализирует не только сгенерированный ответ, но и дополнительный контекст для проверки его качества. Вот несколько примеров таких дополнительных входных данных:

Ответ + Эталонный ответ – полезно, если у вас есть правильный ответ (ground truth) для сравнения.
Ответ + Вопрос – распространено в чат-ботах и системах Q&A, где важно проверить, соответствует ли ответ заданному вопросу.
Ответ + Извлеченный контекст или Вопрос + Извлеченный контекст – используется в Retrieval-Augmented Generation (RAG), когда поиск предоставляет дополнительную информацию для обоснования ответа.

Метод по-прежнему предполагает прямую оценку ответа, но вместо одного входного текста в промпт передаются два или более фрагментов, и модель анализирует их взаимосвязь.

1. Оценка корректности на основе эталонного ответа

Это оффлайн-оценка, в рамках которой LLM сравнивает ответ с «золотым» эталоном. Такой подход отлично подходит для оценки модели на экспериментальном этапе (при итерациях над архитектурой системы), а также для регрессионного тестирования после обновлений модели или промпта.

Например, в системе вопросов и ответов LLM-судья может проверить, насколько новый ответ соответствует ранее одобренному ответу на тот же самый вопрос.

Такой метод оценки корректности является альтернативой детерминированным метрикам, таким как ROUGE, которые измеряют совпадение слов и фраз в двух ответах, а также методам семантического сходства, использующим предобученные модели эмбеддингов для сравнения значений.

Упрощённый промпт: Сравни сгенерированный ОТВЕТ с эталонным РЕФЕРЕНСОМ. Оцени, передаёт ли сгенерированный ответ тот же смысл, даже если формулировка отличается. Верни один из двух ответов: ‘Correct’ или ‘Incorrect’.

Например, в посте в блоге Segment описывалось, как они использовали LLM-оценку для сравнения сгенерированных моделью SQL-запросов с ранее одобренными запросами для того же ввода. Аналогично, Wix использует LLM-судей для проверки корректности ответов в Q&A-системах, сопоставляя их с ground truth.

Пример из блога Wix

2. Оценка качества ответа с учётом вопроса.

В отличие от предыдущего примера, где требуется золотой эталон ответа, можно проводить оценки, используя данные, доступные на момент генерации ответа. Например, в системах вопросов и ответов можно оценивать ответ вместе с вопросом по следующим критериям:

Полнота: Охватывает ли ответ все аспекты вопроса?
Релевантность: Является ли ответ напрямую связанным с заданным вопросом?

Эти типы оценок можно использовать для постоянного мониторинга качества.

Упрощённый промпт: Оцените следующий ОТВЕТ на основе данного ВОПРОСА. Полный ответ — это тот, который полностью охватывает все части вопроса. Верните один из следующих лейблов: ‘Complete’ или ‘Incomplete’.

3. Оценка релевантности контекста в RAG

Retrieval-Augmented Generation (RAG) — это особый случай архитектуры LLM-продуктов.

В такой системе сначала выполняется поиск документов, которые могут помочь ответить на вопрос, а затем они используются для генерации ответа. Это позволяет добавить к ответу LLM актуальные знания. Чтобы корректно оценить эффективность RAG, необходимо проанализировать оба аспекта:

Насколько хорошо система извлекает релевантные документы.
Насколько качественен итоговый ответ.

Для первой части — оценки качества поиска — можно использовать метрики ранжирования, такие как NDCG или precision at K. Эти метрики позволяют количественно оценить, насколько хорошо система находит и сортирует документы, которые помогают ответить на запрос. Такие оценки обычно проводятся офлайн при настройке параметров, например, разных стратегий поиска.

Однако метрики ранжирования требуют разметки релевантности, то есть каждый документ должен быть помечен как полезный или бесполезный для ответа на вопрос. Это можно сделать вручную, но также можно поручить эту задачу LLM. В таком случае модель будет выступать в роли судьи релевантности контекста.

После того как LLM оценит релевантность каждого извлеченного текстового фрагмента по отношению к запросу, эти метки можно использовать на следующем этапе оценки для вычисления метрик качества ранжирования.

Упрощённый промпт: Оцените релевантность текста для ответа на ВОПРОС. Релевантный текст содержит информацию, которая помогает ответить на вопрос, даже если частично. Верните один из следующих лейблов: ‘Relevant’ или ‘Irrelevant’.

Например, этот метод описан в статье “Large Language Models Can Accurately Predict Searcher Preferences” (Thomas et al., 2023).

4. Оценка галлюцинаций в RAG

С другой стороны, особенно когда система RAG уже запущена в продакшен, важно проверять качество финального ответа. Одна из ключевых проблем здесь — галлюцинации: LLM может придумывать детали, которых нет в исходных данных.

Поскольку у вас есть и ответ, и извлеченный контекст, можно выполнить еще одну проверку, чтобы определить, насколько обоснован ответ. По сути, создаётся судья достоверности, который проверяет, корректно ли LLM обработал извлеченный контент.

Упрощённый промпт: Оцените, насколько ОТВЕТ соответствует КОНТЕКСТУ. Достоверный ответ должен включать только информацию, присутствующую в контексте, избегать придумывания новых деталей и не противоречить исходным данным. Верните один из следующих лейблов: ‘Faithful’ или ‘Not Faithful’.

Например, в блоге DoorDash о RAG-основанном агенте поддержки они упоминали использование LLM-судей для оценки согласованности ответа с контекстом.

Этот подход также можно применять для оценки рефератов и суммаризаций, сравнивая их с оригинальным источником. Это помогает выявлять несоответствия и проверять качество обобщения. Например, в одной из научных работ исследуются методы обнаружения фактических ошибок в суммаризациях.

Лучшие подходы для LLM-судей

LLM-оценщики — это мощный инструмент, но их настройка требует четкого подхода к вашему конкретному сценарию. Вам потребуется время, чтобы спроектировать и протестировать судью, создать качественные промпты и выстроить надежный pipeline для непрерывного мониторинга.

Рассмотрим некоторые ключевые практики.

Как создать LLM-судью

Коротко: Создание LLM-судьи — это небольшой ML-проект. Начните с размеченного датасета, который отражает желаемые критерии оценки, затем разработайте и уточните evaluation prompt, чтобы обеспечить соответствие разметке.

Создание LLM-судьи похоже на разработку любого LLM-продукта — вам нужен промпт, который четко объясняет модели, что делать. В данном случае это оценочный промпт, инструктирующий LLM, как анализировать текстовые входные данные и возвращать метку, оценку или объяснение.

Но здесь есть нюанс: если вы используете LLM для оценки других LLM и результат не является детерминированным, как убедиться, что судья соответствует вашим ожиданиям?

Придётся использовать итеративный подход, оттачивая LLM-судью так же, как и промпты для основного LLM-продукта. Другими словами, ваша система оценки сама нуждается в оценке!

Вот описание общего процесса:

Шаг 1. Определите сценарий оценки.

Сначала решите, что именно должен оценивать LLM-судья. Вы проверяете корректность, тон, лаконичность или что-то другое? Можно оценивать только сгенерированный ответ, сравнивать несколько входных данных или анализировать полный транскрипт диалога. Начните с постановки цели, а затем подумайте, как формализовать процесс оценки.

Совет: Не плодите лишнего. Не пытайтесь оценивать слишком много параметров одновременно. Если вам нужно проверить несколько аспектов (например, тон и точность), разделите их на отдельные оценки. По возможности используйте четкие бинарные критерии (например, «правильно» или «неправильно»).

Шаг 2. Подготовьте датасет для оценки.

Теперь создайте небольшой датасет для тестирования LLM-судьи. Он может включать примеры из экспериментов или продакшн-данных. Если у вас их нет, можно сгенерировать синтетические примеры, имитирующие ожидаемые входные данные.

Объем датасета не обязательно должен быть большим, но он должен включать разнообразные примеры, особенно те, которые ставят под сомнение ваши критерии оценки.

Шаг 3. Разметьте этот датасет.

Вот ключевой момент: вам нужно вручную разметить датасет так, как это будет делать LLM-судья. Этот размеченный набор данных станет вашей «эталонной правдой» (ground truth) и поможет измерить качество работы модели.

Кроме того, разметка вручную заставит вас чётко определить, какие аспекты оценки важны. Это напрямую повлияет на формулировку инструкций для LLM.

Допустим, вы создаете судью для проверки корректности, который будет сравнивать два LLM-ответа — например, старый и новый вариант после обновления модели. Вам нужно решить, какие изменения имеют значение: вы оцениваете, насколько ответы совпадают в целом, или учитываете конкретные детали? Возможно, какие-то изменения, например добавление приветствия, допустимы, но предложение дополнительного действия — уже нет. Часто оцениваются конкретные типы ошибок, такие как противоречия, пропуски, изменение порядка или стиль ответа.

Например, в этом сценарии мы считали любую дополнительную информацию (например, предложение пользователю обратиться к агенту) некорректной, даже если она напрямую не противоречила референсному ответу.

Детали могут отличаться в зависимости от ваших задач!

Вы, конечно, можете пропустить этот шаг: просто напишите лучший возможный prompt, затем посмотрите и исправьте метки, сгенерированные LLM, чтобы создать утвержденный тестовый датасет. Однако часто начинать с ручной разметки полезнее — это помогает улучшить ваш изначальный prompt.

Шаг 4. Создайте промпт для оценки.

Когда вы точно знаете, что хотите оценивать, пора составить промпт для LLM-судьи.

Например, вот как мы сформулировали критерии оценки после ручной разметки данных для примера с проверкой корректности. Эти критерии учитывают конкретные типы ошибок, которые мы обнаружили:

Мы поделимся дополнительными советами по написанию промптов в следующем разделе.

Шаг 5. Оценка и итерации.

Когда ваш промпт готов, примените его к оценочному датасету и сравните выводы LLM-судьи с вашей вручную размеченной эталонной правдой (ground truth).

Для бинарных классификаций можно использовать метрики, такие как precision и recall, чтобы измерить, насколько хорошо работает LLM-судья, особенно если фокусировка идёт на определённый класс.

Например, если приоритет — обнаружение «некорректных» ответов, даже ценой некоторых ошибок в классификации «корректных» ответов, тогда recall будет ключевой метрикой качества.

Если результаты не соответствуют вашим ожиданиям, можно скорректировать промпт и повторно запустить оценку. В идеале стоит выделить часть размеченного вручную датасета в "held-out" набор, который будет использоваться только для финального тестирования промпта.

Важно понимать, что LLM-судья не обязан быть идеальным — достаточно, чтобы он был "достаточно хорош" для ваших целей. Даже человеческие эксперты делают ошибки!

Привлекайте экспертов предметной области

Нетеxнические специалисты — такие как продакт-менеджеры или эксперты в предметной области — играют ключевую роль в настройке оценочных критериев. Они помогают определить, какие аспекты поведения или темы должен фиксировать LLM, формируют рекомендации для промптов и тестируют их соответствие ожиданиям. Благодаря no-code инструментам этот процесс можно сделать коллаборативным.

Создание LLM-судей — это итеративный процесс. Особенно если вы начинаете с синтетического датасета, со временем, анализируя реальные пользовательские данные, вам может потребоваться пересмотреть стандарты, скорректировать промпт судьи или разбить критерии на более узкие категории.

В статье “Who Validates the Validators?” (Shankar et al., 2024) авторы обсуждают дрейф критериев (criteria drift), когда ручная разметка выходных данных LLM помогает пользователям уточнять свои ожидания на основе конкретных ошибок и паттернов, обнаруженных в ответах модели.

Промпты для оценки LLM

Коротко: Пишите собственные промпты. Используйте вопросы с ответами «да/нет» и разбивайте сложные критерии. Запрос объяснения помогает повысить качество оценки и упростить отладку.

Промпты для оценки — это основа вашего LLM-судьи. Как и в случае с человеческими экспертами, вам нужно предоставить LLM точные, детализированные инструкции.

Идеально, если вы будете писать собственные промпты для оценки. Во-первых, именно здесь LLM-судьи проявляют себя наилучшим образом — их можно адаптировать под конкретные задачи. Во-вторых, даже небольшие уточнения могут повысить качество сценария по сравнению с использованием универсального промпта.

Помните, что промпты ведут себя по-разному в разных моделях. Одна и та же инструкция для оценки может давать разные результаты на GPT-4.0 mini и GPT-4.0. Критически важно тестировать их в сравнении с ожидаемыми результатами.

Совет: Если вы используете внешние инструменты оценки, основанные на LLM-метриках, всегда проверяйте их промпты и тестируйте их на ваших метках, чтобы убедиться, что они соответствуют вашим требованиям.

Существует несколько техник промптинга, которые могут повысить точность и согласованность вашего LLM-оценщика. Они аналогичны тем, что применяются при разработке LLM-продуктов, например, chain of thought prompting. Просьба к LLM оценить текст не отличается от других задач, которые вы ему поручаете — те же методики работают и здесь.

Вот несколько подходов, которые стоит рассмотреть.

1. Используйте бинарную или низкоточную шкалу оценки

Бинарные оценки, такие как «вежливый» vs. «невежливый», обычно более надежны и согласованы как для LLM, так и для человеческих оценщиков. Гораздо проще получить точные результаты при выборе из двух простых категорий, чем пытаться определить, соответствует ли ответ 73 или 82 баллам по шкале «вежливости».

Хотя специализированные вероятностные модели машинного обучения могут выдавать такие точные оценки, LLM генерируют текст и не имеют встроенной калибровки для высокоточных шкал.

Можно также использовать трехуровневую систему, например: «релевантно», «нерелевантно» и «частично релевантно», или добавить вариант «неизвестно», если информации недостаточно. Это помогает избежать вынужденных решений при нехватке данных.

2. Объясняйте значение каждой оценки

Подходите к написанию промптов так, как если бы давали инструкции стажеру, выполняющему задачу впервые. Не просто просите LLM пометить что-то как «токсичное» или «не токсичное» — дайте чёткое определение «токсичности», например: язык, содержащий оскорбительные или вредоносные выражения определённого типа.

Если ваша цель — отметить все пограничные случаи, а не упустить их, можно задать более строгие инструкции: «Если есть сомнения, лучше считать текст токсичным».

Четкое определение классов особенно важно при использовании 5-балльной шкалы. В чём разница между 3 и 4? Если это неочевидно, то и LLM, и человеческие аннотаторы будут испытывать трудности с консистентностью оценок.

Если такие различия сложно объяснить, это может быть признаком того, что шкалу стоит упростить. Без чётких инструкций LLM может давать несогласованные результаты: по-разному оценивать схожие тексты или смещаться в сторону оценок, которые чаще встречались в его обучающих данных.

Чтобы снизить вариативность, можно использовать множественные оценки, а затем агрегировать их методами max voting (голосование большинством) или усреднения. Например, см. работу “Replacing Judges with Juries” (Verga et al., 2024).

Хотите проверить, насколько ваши инструкции хороши? Попробуйте сами разметить несколько ответов! Если вам потребуется дополнительное пояснение для принятия решения, то, скорее всего, и LLM столкнётся с той же проблемой. А если инструкции получаются слишком детализированными, подумайте о разбиении задачи на несколько этапов.

3. Упрощайте оценку, разбивая критерии

Если необходимо оценить несколько аспектов, таких как полнота, точность и релевантность, лучше разнести их на отдельные метрики. Это делает процесс более структурированным и сфокусированным.

Результаты всё равно можно объединять детерминированным способом. Например, можно флагировать ответ, если хотя бы один из критериев получил отрицательную оценку (неполный, неточный или нерелевантный). Другой вариант — суммировать количество «хороших» оценок для формирования итогового балла или использовать взвешенное объединение, отражающее значимость каждого критерия.

Такой подход позволяет LLM анализировать по одному признаку за раз, а не пытаться сразу учитывать сложные взаимосвязи. Это повышает точность оценки и облегчает её верификацию со стороны человека.

4. Добавляйте примеры в промпт

Если ваши критерии сложны и требуют тонких различий, стоит включить примеры входных данных и их оценок.

Промпт может начинаться с общего объяснения (например, «Хорошо» означает…, «Плохо» означает…), а затем содержать примеры как хороших, так и плохих ответов. Такие примеры помогают LLM лучше понять, как применять критерии — особенно в случае менее мощных моделей.

Этот метод называется few-shot learning. Подробнее о нём можно прочитать в статье “Language Models are Few-Shot Learners” (Brown et al., 2020).

Однако важно протестировать, как добавление примеров влияет на работу модели. Несбалансированные или предвзятые примеры могут искажать её решения.

Например, если в промпте больше негативных примеров, чем позитивных, или если все негативные примеры расположены в конце, порядок и частота могут повлиять на оценки. Хотя для более продвинутых моделей этот эффект выражен слабее.

Дополнительные исследования на эту тему приведены в статье “Calibrate Before Use: Improving Few-Shot Performance of Language Models” (Zhao et al., 2021), где показано, что результаты few-shot обучения зависят от таких факторов, как формат промпта и порядок примеров.

5. Поощряйте пошаговое рассуждение

Как и в других задачах, если попросить LLM «размышлять» над процессом перед тем, как дать окончательный ответ (подход, известный как Chain of Thought (CoT)), это может значительно повысить качество результатов.

Идея добавления этапа рассуждения в промпт подробно исследуется в статье “Chain-of-Thought Prompting” (Wei et al., 2022), а также в работе “Large Language Models are Zero-Shot Reasoners” (Kojima et al., 2022).

Примеры различных Chain-of-Thought промптов из статьи Kojima et al.

Вы можете использовать тот же подход в своем evaluation prompt: попросите модель объяснить своё рассуждение или думать шаг за шагом, фактически реализуя Zero-Shot-CoT. В этом случае модель будет выдавать как обоснование, так и итоговый результат в одном ответе. Дальнейшие исследования показывают, что это значительно повышает качество оценок.

Кроме того, этот метод создаёт trace логики, который можно впоследствии проанализировать. Это особенно полезно при траблшутинге во время разбора ответов. Например, сгенерированное обоснование может показать, какие части текста привели модель к тому, чтобы пометить его как некорректный или содержащий лично идентифицируемую информацию (PII).

Multi-turn Chain of Thought: Некоторые исследователи изучают более сложные подходы к CoT. Например, одна из методик — G-Eval (см. Liu et al., 2023) — использует процесс, в котором ИИ сначала определяет задачу, затем планирует шаги, а потом заполняет форму оценки. Однако дальнейшие исследования показывают, что такая автоматически сгенерированная CoT-логика не всегда дает лучшие результаты. Напротив, простой запрос к LLM на объяснение или анализ зачастую превосходит этот метод (см. Chiang et al., 2023).

6. Установите низкую температуру

В LLM temperature контролирует степень случайности вывода. Высокие значения приводят к большему разнообразию ответов, тогда как низкие делают генерацию более предсказуемой. При оценке вам не нужна креативность — установите низкую температуру, чтобы модель давала стабильные ответы на одинаковый ввод.

7. Используйте более мощную модель

При проведении оценки логично начинать с более сильной модели. Это, как правило, повышает согласованность ответов с человеческими суждениями. Получив надежную референтную точку, можно экспериментировать с меньшими или менее мощными моделями, чтобы понять, удовлетворяют ли они вашим требованиям.

8. Получайте структурированные выходные данные

Наконец, всегда стремитесь к структурированному формату вывода, например, JSON. Это значительно упрощает разбор и дальнейший анализ результатов оценки.

Итоги. Подытожим основные рекомендации по написанию evaluation prompts:

Используйте бинарные или низкоточные оценки.
Разбивайте сложные критерии на отдельные метрики.
Чётко определяйте значение каждой оценки или метки, добавляйте примеры.
Просите LLM думать шаг за шагом и объяснять свою логику.
Устанавливайте низкую температуру.
Используйте более мощную модель, если это возможно.

Начинать лучше с простых подходов. Пока вы оцениваете, насколько эффективно работает ваш LLM-судья, можно придерживаться базового метода и использовать его, если он дает устойчивые результаты. Если же вы хотите поэкспериментировать с более сложными техниками (например, многошаговой цепочкой рассуждений с дополнительными LLM-вызовами), этот базовый подход станет хорошей отправной точкой.

Для более глубокого обзора исследований по теме LLM-as-a-judge рекомендуем отличные материалы Cameron Wolfe и Eugene Yan.

Наблюдение за LLM в продакшене

Коротко: LLM observability помогает отслеживать производительность системы в продакшене. Настройте tracing для сбора live-данных, запланируйте оценки, в которых части этих данных оцениваются LLM-судьями, и используйте дашборд для мониторинга трендов, таких как разочарование пользователей и галлюцинации модели.

Когда ваша система выходит в продакшен, реальные пользователи начинают взаимодействовать с ней самыми непредсказуемыми способами. Даже самый тщательный pre-launch тестинг не охватит все возможные сценарии использования. Поэтому отслеживание фактической производительности в реальном времени крайне важно.

В продакшене нет идеального эталонного ответа, с которым можно напрямую сравнивать выводы модели, поэтому мониторинг качества необходимо проводить по самим ответам. В этом помогают LLM-оценщики. Вы можете настроить регулярную проверку новых генераций по выбранным критериям.

Этот мониторинг нужен не только для контроля качества — он также даёт ценные инсайты о том, как пользователи взаимодействуют с вашим инструментом, какие задачи и темы встречаются чаще всего.

Как настроить процесс:

1. Tracing

Первый шаг — tracing: сбор данных о взаимодействиях пользователей и их сохранение для последующего анализа. Вам нужно инструментировать своё ИИ-приложение так, чтобы фиксировать входные данные, вызовы LLM и функций, а также генерируемые ответы.

Основное преимущество очевидно: имея логи, вы сможете просматривать и анализировать их, чтобы понимать, что именно происходит, когда пользователи взаимодействуют с вашей системой.

Изначально вы можете вручную просматривать ответы, чтобы выявлять общие шаблоны или проблемы. Однако по мере роста объёма данных ручная проверка перестаёт масштабироваться, поэтому потребуется некоторая автоматизация.

2. Планирование оценок

Когда у вас уже есть сохраненные данные трассировки и вы понимаете, что именно хотите оценивать, можно настроить запланированные оценки. Это регулярные проверки, в ходе которых новые ответы или полные диалоги прогоняются через LLM-судью, чтобы определить, как система справляется с задачами на основе заданных вами атрибутов. Если вы обрабатываете большой объём данных, можно использовать выборку подмножеств для отслеживания производительности.

Например, если у вас работает чат-бот для клиентской поддержки, можно оценивать 10% диалогов на предмет признаков фрустрации пользователя, повторяющихся вопросов или запросов без выполнения. Регулярное проведение таких оценок (например, каждый час, день или после X диалогов) позволит вам оперативно отслеживать производительность системы.

Также можно комбинировать оценку при помощи LLM с другими методами, например, использовать регулярные выражения для поиска конкретных терминов, таких как упоминания продуктов или конкурентов.

3. Создание дашборда

Для удобного отслеживания производительности вам понадобится дашборд.

После проведения оценки на последней партии данных можно добавлять метрики, такие как «доля ответов, отмеченных как галлюцинации» или «количество диалогов, в которых пользователи выразили фрустрацию», и визуализировать их в динамике. Это поможет отслеживать тенденции производительности и выявлять возможные проблемы.

Также можно настроить оповещения, чтобы в случае отклонения показателей от нормы вы получали уведомления немедленно и могли вмешаться до того, как проблема затронет слишком многих пользователей.

4. Анализируйте свои данные!

Мониторинг и отладка всегда идут рука об руку. Например, если вы замечаете рост пользовательской фрустрации, стоит пересмотреть конкретные проблемные диалоги. Можно экспортировать примеры для fine-tuning модели или создать тестовый набор, чтобы скорректировать промты и устранить проблему.

Кроме того, после запуска LLM-судьи важно регулярно его проверять. Даже если LLM-оценка изначально выстроена правильно, со временем могут измениться либо сам оценщик, либо ваши ожидания, поэтому может потребоваться настройка или добавление новых методов оценки, чтобы система оставалась стабильной.

Плюсы и минусы

Коротко:
Плюсы — разумное качество, высокая скорость, простота обновлений и возможность проводить оценки по пользовательским критериям без эталонных данных.
Минусы — несовершенное качество, потенциальные biasы и затраты/риски при использовании внешних LLM.

LLM-судьи — это практичное и масштабируемое решение для оценки выходных данных ИИ, но они имеют свои компромиссы. Вот их основные плюсы и минусы.

Плюсы LLM-судей

Высокое качество оценок. При правильной настройке LLM-судьи дают результаты, близкие к человеческим оценкам, и обеспечивают их стабильность. Для задач большого масштаба это часто единственная реалистичная альтернатива ручному анализу.
Не требуется эталонный ответ. LLM-судьи могут анализировать сгенерированные выходные данные без необходимости сравнения с заранее заданными эталонными ответами. Это делает их идеальными для мониторинга в реальном времени на продакшене.
Гибкость. Можно настроить промпты для оценки чего угодно — от полезности ответа до соответствия фирменному стилю, фактически назначая задачи ИИ так же, как и людям.
Масштабируемость. После настройки LLM-судьи могут обрабатывать тысячи оценок в режиме 24/7, что значительно быстрее и дешевле, чем работа человеческих аннотаторов. Их оперативность и доступность в реальном времени сложно превзойти. Кроме того, легко масштабировать оценки на разные языки.
Простота обновления. LLM-судьи легко адаптируются к изменениям в продукте и критериях оценки. Достаточно обновить промт — нет необходимости переучивать модель, как в классическом машинном обучении.
Вовлечение экспертов. Поскольку LLM-судьи работают на естественном языке, даже нетехнические специалисты могут участвовать в написании промптов и анализе результатов, помогая учесть важные нюансы.

Минусы LLM-судей

Хорошо, но не идеально. LLM-судьи не безупречны. Если инструкции слишком размытые или сложные, качество оценок может страдать. LLM также может давать непоследовательные суждения для одного и того же ввода, хотя повторный опрос (задавание одного и того же вопроса несколько раз) помогает сгладить этот эффект. Важно управлять ожиданиями и регулярно отслеживать качество.

Риски bias'ов. LLM обучаются на огромных наборах данных, что может вносить bias в их оценки. Например, если попросить классифицировать текст как «профессиональный» или «непрофессиональный» без чётких критериев, модель будет полагаться на предположения из обучающих данных. В парных оценках известны следующие bias'ы (см. Zheng, L., 2023):

Позиционный bias: предпочтение ответов в зависимости от их расположения (например, первого или последнего).
Болтливость (verbosity bias): склонность отдавать предпочтение более длинным ответам, даже если они не точнее.
Эффект самопреумножения (self-enhancement bias): предпочтение текстов, сгенерированных тем же самым LLM.

Эти bias'ы можно минимизировать, меняя порядок ответов или используя систему прямого скоринга, вместо того чтобы просить LLM выбрать «лучший» ответ.

Конфиденциальность данных. Использование сторонних LLM API для оценки означает передачу данных внешним сервисам. Хотя это часто допустимо (учитывая, что ответы уже генерируются через LLM), важно учитывать вопросы приватности и безопасности, особенно если работа ведётся с чувствительными данными.

Не мгновенно. Хотя LLM работают быстрее людей, они всё же медленнее и дороже по сравнению с rule-based проверками или более лёгкими ML-моделями. Это делает их менее подходящими для задач типа guardrails, где необходимо мгновенно валидировать ответ во время генерации.

Не бесплатно. Запуск оценок на мощных LLM может быть дорогим, особенно при больших датасетах или множественных запросах на каждую генерацию. Можно сократить расходы, комбинируя LLM-суждения с более простыми методами, такими как регулярные выражения, выборочная проверка данных и оптимизированные промты (например, проводить одну комплексную оценку вместо нескольких этапов).

Требует настройки. Развернуть эффективного LLM-судью — это работа. Нужно чётко определить критерии, разработать промпты для оценки и разметить датасеты. Это не решение в стиле set-it-and-forget-it — потребуется периодическая проверка и настройка, чтобы судья продолжал работать качественно.

Альтернативы LLM-судьям

Коротко: Другие варианты включают ручную разметку, сбор пользовательской обратной связи, использование специализированных ML-моделей и rule-based проверок. Если доступен эталонный ответ, можно применять метрики вроде ROUGE. Оптимальный подход обычно сочетает несколько методов.

Хотя LLM-судьи — это мощный инструмент, они не единственный способ оценивать выходные данные ИИ. Вот несколько альтернативных методов:

Ручная разметка. Человеческая оценка остаётся золотым стандартом, особенно для сложных или субъективных задач. Этот метод плохо масштабируется, но начинать с ручных проверок почти всегда имеет смысл. Это позволяет выявить ключевые шаблоны поведения модели и определить, что именно нужно отслеживать. После того как критерии оценки четко сформулированы, их можно автоматизировать с помощью LLM. Однако полностью отказываться от ручной проверки не стоит — полезно периодически анализировать часть текстов вручную.

Сбор пользовательской обратной связи. Не забывайте о пользователях! Можно попросить их оценить качество ответа LLM прямо в интерфейсе, например, сразу после генерации. Это дает реальную обратную связь от людей, которые непосредственно взаимодействуют с системой.

Специализированные ML-модели. Для чётко определённых задач традиционные модели машинного обучения могут быть весьма эффективными. Доступно множество предобученных моделей для таких задач, как анализ тональности или детекция персональных данных (PII). Также можно использовать embedding-модели для сравнения семантического сходства текстов, что особенно полезно для регрессионного тестирования при сравнении старых и новых ответов.

Например, можно оценивать тональность текстов с помощью доступных предобученных ML-моделей.

Модели-оценщики с fine-tuning. При использовании LLM в качестве судей вы естественным образом собираете размеченный датасет, который можно использовать для fine-tuning моделей под конкретные задачи оценки. Это может быть отличным долгосрочным решением, если ваши задачи предсказуемы, однако у таких моделей есть ограничения. Как правило, они жестко привязаны к своей схеме оценки и хуже обобщаются при изменении входных данных или критериев (см. статью).

Метрики оценки генеративного качества. Для офлайн-оценки структурированных задач, таких как суммаризация и машинный перевод, можно использовать метрики вроде ROUGE и BLEU, которые объективно измеряют качество ответа, сравнивая его с эталонными текстами. Эти метрики основаны на совпадении n-грамм и оценивают, насколько близок выходной текст к идеальному ответу.

Оценка на основе правил. Иногда самые простые методы оказываются наиболее эффективными. Использование правил, например, поиск определенных ключевых слов или фраз (таких как "cannot" или "apologies" для отказа, а также списка нецензурных выражений), позволяет быстро выявлять очевидные ошибки. Этот метод быстрый, дешевый и простой, а главное — детерминированный, так что его результаты полностью предсказуемы.

Комбинированный подход. На практике обычно комбинируются различные методы. Например, можно сначала применять систему на основе правил для фильтрации очевидных ошибок, а затем использовать LLM-судей или человеческую экспертизу для более сложных и тонких оценок.

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14 мая 2025 г.

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11.03.25 16:25 ricciordonez

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12.03.25 05:35 cholevasaca

As the senior teacher at Greenfield Academy, I wanted to share our ordeal with TECH CYBER FORCE RECOVERY after our school was targeted by a malicious virus attack that withdrew a significant amount of money from our bank account. A third-party virus infiltrated our system and accessed our financial accounts, resulting in a USD 50,000 withdrawal. This attack left us in a state of shock and panic, as it posed a serious threat to our school's financial stability. Upon realizing what had happened, we immediately contacted TECH CYBER FORCE RECOVERY for help. Their team responded promptly and began investigating the situation. They worked tirelessly to track down the virus, analyze its behavior, and understand how it had bypassed our security measures. Most importantly, they focused on recovering the funds that had been stolen from our bank account. Thanks to TECH CYBER FORCE RECOVERY's quick and expert intervention, they were able to successfully recover all the funds that had been withdrawn. Their team worked closely with our bank and utilized advanced recovery methods to ensure the full amount was returned to our account. We were incredibly relieved to see the stolen funds restored, and their efforts prevented any further financial loss. I am incredibly grateful for TECH CYBER FORCE RECOVERY's professionalism, expertise, and swift action in helping us recover the stolen funds. Their team not only managed to undo the damage caused by the virus but also ensured that our financial security was restored. I highly recommend their services to any organization dealing with similar cyberattacks, as their team truly went above and beyond to resolve the issue and protect our assets. CONTACTING THEM TECH CYBER FORCE RECOVERY EMAIL. [email protected]
14.03.25 21:40 adelfinalongo

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15.03.25 00:29 irenmroma

CRYPTOCURRENCY RECOVERY FIRM DUNAMIS CYBER SOLUTION
15.03.25 00:29 irenmroma

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15.03.25 14:34 spencerwerner

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17.03.25 02:30 [email protected]

"Work smart and not hard" that's these scammers' slogan. They lure you with promises of luxury and lifetime riches without you doing anything besides investing in their investment schemes, and if you ever fall prey, they will siphon you of every penny dry. I fell victim to it, but was fortunate to be helped by the best recovery expert (TRUST GEEKS HACK EXPERT). They are more than just recovery specialists; they are allies in the fight against online fraud. Trust their expertise and let them guide you towards reclaiming control over your financial future.for assistance, visit website https://trustgeekshackexpert.com/ Wh@t's A p p +1-7-1-9-4-9-2-2-6-9-3 <> E mail: Trustgeekshackexpert{@}fastservice{.}com
17.03.25 12:20 browne

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17.03.25 13:14 vinnypraise

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17.03.25 13:14 vinnypraise

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17.03.25 15:23 Charlesagrimes

Running the small business was hard enough in itself without having to suffer such stress as loss of access to a Bitcoin wallet. I had put $532,000 into Bitcoin for my business in case of emergencies, but one day, the wallet was nowhere to be seen. I had trusted it to my friend while he managed my financials, little knowing he would prove less than trustworthy. I couldn't envision the gut-wrenching feeling coupled with a great feeling of folly for having placed so much trust in someone else's hands. I could not believe my eyes to see that all of my digital fortune was gone. Literally, every second of my busy day froze in disbelief as I tried to conceive the loss. I remembered the hours I had put into building my business, securing my investments, and planning for the future. Now, I was staring at an empty wallet that once held my safety net. This hit me hard-not only had I lost $532,000, but the betrayal hurt further because it came from someone I considered reliable. The emotional toll was huge, and this financial hit could cripple my business. In the midst of my despair, I knew I had to act fast. I went online, searching everywhere for a solution through which I could get back in control of my finances. That is where I came across Digital resolution services. I knew them for recovery related to lost crypto, but also somehow skeptical. At that point, it looked like I had nothing left to lose. I reached out, and with our very first conversation, I gained the impression that they did understand my situation. Their team was nothing short of extraordinary. They approached my case with professionalism and empathy, meticulously explaining the recovery process in clear, simple terms that alleviated some of my anxiety. They set realistic expectations while promising to do everything possible to retrieve my funds. Over the next several weeks, they worked tirelessly, employing advanced blockchain forensic techniques and sophisticated tracking tools that I’d never even heard of before. It kept me informed with constant updates and gave me hope in this desperate time. Finally, it came-the day Digital resolution services recovered my lost Bitcoin. Relief overwhelmed me, and it wasn't all about the money but control and self-trust in my financial future. This experience taught me the most valuable lesson: never lose control over your own financial security and never put blind trust in anyone. With Digital resolution services, I recovered not only my $532,000 but also learned how to protect my assets better in the future. Contact Digital Resolution Services: Email: digitalresolutionservices (@) myself. c o m WhatsApp: +1 (361) 260-8628 Stay protected Charles agrimes
18.03.25 23:49 lindaspringer311

My name is Linda Springer, and I am part of an ultra-high-net-worth family in the United Kingdom. Managing our wealth has always been a top priority, and I’ve always looked for opportunities to grow and diversify our investments. When I was introduced to an offshore banking investment program called Market Fund, it seemed like the perfect opportunity to enhance our portfolio. The program promised impressive returns, and with professional advisors and seemingly legitimate documentation, I felt confident that it was a secure investment. However, my trust in Market Fund quickly proved to be misplaced. Initially, everything seemed to go smoothly. Reports showed strong growth, and I was reassured by the continuous updates from the program’s advisors. But when I tried to withdraw my funds, everything took a sharp turn. The Market Fund website became unresponsive, and all my attempts to reach the advisors through emails and phone calls went unanswered. It was then that I realized I had fallen victim to a scam, and the £60,000 I had invested was gone. Feeling devastated and unsure of what to do next, I turned to a friend I had met at a club called The Vault. During one of our conversations, Alex, a fellow club-goer, mentioned how Rapid Digital Recovery had helped them recover funds from a similar fraudulent scheme. Intrigued by their success story, I decided to reach out to Rapid Digital Recovery....Whatsapp: +1 4 14 80 71 4 85.. hoping they could help me recover my money as well. From the moment I contacted them, the team at Rapid Digital Recovery took immediate action. They began investigating the scam and used advanced technology to trace the origins of Market Fund. Through their efforts, they uncovered a network of fake websites and shell companies that had been designed to deceive investors like myself. Thanks to their persistence and expertise, Rapid Digital Recovery successfully recovered the £60,000 I had lost.....Email: rapid digital recovery (@) execs. com.. Not only did they restore my financial security, but they also exposed the fraudulent operation behind Market Fund, preventing others from falling victim to the same scam. While the experience was incredibly stressful, it served as an important reminder of the need for due diligence and professional oversight when dealing with offshore investments. I am incredibly grateful to Rapid Digital Recovery for their support, expertise, and determination in recovering my funds. Telegram: h t t p s: // t. me /Rapiddigitalrecovery1
20.03.25 17:21 katherineingram

Dr. Katherine Ingram
20.03.25 17:21 katherineingram

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20.03.25 19:34 ysabelbristol

I am YSABEL, a medical student living with my grandmother. For the past few years, I’ve been diligently saving my grandmother’s money to ensure she has a comfortable and secure future. I invested over $40,000 into an online platform, which promised significant returns. After completing several steps, they told me that to withdraw the money, I needed to make another payment of $73,000. As a medical student, my time is already stretched thin, and managing my grandmother’s finances on top of my studies was overwhelming. That's when a costudent, who had gone through a similar experience, recommended MUYERN TRUST HACKER. Initially, I was skeptical. After all, how could anyone help me recover the money I had already lost to such a sophisticated scam? But after reaching out to them, I quickly realized I was in good hands. From the very first conversation, the team at MUYERN TRUST HACKER was professional, compassionate, and knowledgeable. They understood the gravity of my situation and took the time to thoroughly review all the details of my case. They explained the recovery process clearly, step by step, and assured me that they would work tirelessly to help recover the funds I had lost. Their expertise in dealing with online fraud was evident, and for the first time in weeks, I felt a sense of hope that I could regain control over this situation. The recovery process wasn’t immediate, and there were moments of uncertainty, but MUYERN TRUST HACKER remained dedicated and persistent. As someone who has been working hard to save and protect my grandmother’s money, it was devastating to feel like I might lose everything to a scam. But MUYERN TRUST HACKER gave me the support, expertise, and guidance I needed to recover what was lost. I’m deeply grateful for their help, and I now have a renewed sense of hope that I can protect my grandmother’s future. If you find yourself in a similar situation, feeling trapped and overwhelmed by a fraudulent company, I highly recommend MUYERN TRUST HACKER. (Whats A p p at + 1 (440) (335) (0205) Their team provides real solutions, and their professionalism and dedication were a lifeline for me during a very difficult time. Thanks to them, I feel confident that I can move forward and continue to protect my grandmother’s savings. Alternatively, contact their tele gram also if you need immediate attention at muyerntrusthackertech.
21.03.25 21:19 carmenbeechum643

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22.03.25 14:13 [email protected]

The glow of RGB lights still haunts me. There I was, mid-stream, hyping up a Fortnite squad when an email pretending to be a sponsorship opportunity with the subject line "ENERGY DRINK COLLAB!!! *" appeared on my second monitor. I clicked— big mistake. By the time my chat spammed "*SCAM ALERT" in neon caps, a trojan had already ghosted my Bitcoin wallet, $320,000 gone, poof, like a noob disconnecting mid-game. My facecam caught the exact moment my soul left my body: jaw open, headset tilted, the background of anime posters judging me silently. The VOD blew up. Of course, it did. Pandemonium erupted. Donation alerts became panic emojis. My mods DM'd links to "HOW TO FIX CRYPTO THEFT" amidst banning trolls. My wallet? A barren wasteland. My DMs? A cemetery of "*F"s and crypto-bros pitching recovery scams. Then, a lifeline—a chatter named *xX_Cryptosolution_69 typed, "TRUST GEEKS HACK EXPERT. THEY CLAPPED A HACKER FOR MY DOGE ONCE." Desperate, I Googled them mid-stream, muting to scream into a pillow. TRUST GEEKS HACK EXPERT team responded like NPCs scripted for heroics. “Send us the malware file,” they said. “**And your wallet logs. We’ll handle the rest.” For 12 days, they reverse-engineered the trojan, dissecting its code like speed runners cracking a glitch. The virus, it turned out, was a knockoff ransomware dubbed “Crypto rush” (its dev had left a “HACK THE PLANET!!” Easter egg in the code, cringe). TRUST GEEKS HACK EXPERT squad traced its path, resurrecting private keys from registry fragments and backup clouds I’d forgotten existed. The return stream was record-breaking. I rebooted my rig, wallet restored, and titled the stream "HOW I UNBRICKED $320K (AND MY CAREER)." Chatters donated Bitcoin out of solidarity, and schadenfreude. Even my rival streamer, DrL33tGamer, raided me with 10k viewers. TRUST GEEKS HACK EXPERT? They viewed anonymously and left a sub with the message: "GG EZ. These internet Gandalfs didn't just fix a hack—they authored the greatest plot twist in my online existence. Now, my new website, Stream Vault, runs on a server guarded like Fort Knox, and I vet sponsors like the CIA. That fake energy drink company? Its domain now points to a Rickroll. If your crypto gets pawned by a script kiddie, skip the rage quit. Ping the TRUST GEEKS. They're the ultimate cheat code for catastrophe. Just maybe have a malware scanner in closer proximity than your energy drinks next time. (CONTACT SERVICE ) Email, Trustgeekshackexpert[At]fastservice[Dot]com Telegram, Trustgeekshackexpert Email, [email protected] Website, www://trustgeekshackexpert.com
23.03.25 12:24 maryjul75

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23.03.25 22:01 joaneldnde

The ground trembled like a nervous intern on espresso shots. One minute, I was monitoring my geothermal Bitcoin miners, humming in harmony with Iceland's most unpredictable volcano. Next? An eruption painted the sky gray with ash, raining destruction like an out-of-control blockchain fork. Power cables flickered out. Servers turned into abstract-art pieces. And my wallet with $460,000 worth of mining revenue fried faster than a motherboard in a tidal wave of lava. I was knee-deep in volcanic mud, clutching the charred wallet, wondering if the universe had a vendetta against renewable energy. For weeks, I’d played geothermal gambler, harnessing Earth’s anger to mine crypto. Now, Mother Nature had countered with a literal power move. My wallet’s backups? Corrupted by ash-clogged drives. My cold storage? Warmer than a freshly erupted fissure. Even the volcanologists on my team shrugged. “We predict lava, not ledger errors,” one said, handing me a business card signed at the edges. “Try these Cyber Constable Intelligence. They’ve fixed crypto in weird places.” Cyber Constable Intelligence phoned on the first ring. Cyber Constable Intelligence saved not just crypto. They demonstrated that even the fury of nature cannot surpass human tenacity. My operation now operates robustly, excavating coins with Earth's anger and a backup generator sufficient to run a small glacier. The volcano? Still grumbling. My wallet? Locked inside a fireproof safe, as irony bites sharper than an Icelandic winter. If your crypto somehow gets smothered beneath the pyroclastic ash of life, skip the freak-out. Call the Cybers. They'll dig through lava streams until your cash bubbles up to the surface. Just maybe set up your rigs a few miles closer to the crater next time. If you’re facing a similar problem I highly recommend contacting Cyber Constable Intelligence 📞 WhatsApp:+1 (2 5 2 ) 3 7 8 7 6 1 1 🌐 Website: www cyberconstableintelligence.com 📩 Telegram: https://t.me/cyberconstable
23.03.25 22:18 [email protected]

A few months ago, I stumbled upon a post about a cryptocurrency investment platform that I thought was a good idea for me to invest in crypto, I didn’t realize I was being catfished by the cryptocurrency investment manager who promised me huge returns on my investment. I lost my capital of $170,000 and interest without receiving any profits in return, I was devastated And I realized I had been scammed out of my cryptocurrency. I felt hopeless and didn’t know where to turn. i searched everywhere for a solution and That’s when my friend introduced me to a Crypto recovery platform Trust Geeks Hack Expert. At first, I was skeptical—after all, I had just been scammed—but Trust Geeks Hack Expert professionalism and transparency reassured me. They took the time to analyze my case, track my lost funds, and guide me through the recovery process. To my amazement, they successfully retrieved my stolen crypto! I can’t express how grateful I am to Trust Geeks Hack Expert team. Their expertise and dedication gave me a second chance. If you’ve been a victim of a crypto scam, don’t lose hope—Trust Geeks Hack Expert. is the real deal! for assistance, Email: [email protected] (TeleGram:: Trustgeekshackexpert) & what's A p p +1 7 1 9 4 9 2 2 6 9 3
24.03.25 10:03 Diegolola514gmail.com

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25.03.25 18:06 maryjul75

Fast Agent Recovery is a consulting firm that specializes in the recovery of assets from financial fraud. We know how to recover your funds and we have helped thousands of scam victims from around the world to recover their money. If you are a victim of Binary Options fraud, Forex fraud, Bitcoin fraud, Dating scams or one of the many other the online fraudulent practices that permeate the internet then file a complaint on www.fastrecoveryagent,.com to get an assessment if we can help you get your money back too. File a complaint: https://www.fastrecoveryagent.com/
27.03.25 01:52 debrapavon89

The moment my cold storage device flatlined, erasing $385,000 in Bitcoin I’d saved to launch a chain of zero-waste cafés, I became a ghost haunting my own life. I scrolled through forums until my eyes were streaming, trawling through threads filled with such mouthfuls as "irreversible blockchain entropy" and "cryptographic oblivion A Reddit thread finally revealed to me Cyber Constable Intelligence I reached out, the team at Cyber Constable Intelligence took immediate action. They began tracing the fraudulent car auction site and thoroughly investigating the scam. To my relief, after several weeks of hard work, Cyber Constable Intelligence successfully tracked down the scammers and recovered the full $385,000 I had lost. Their hard work allowed me to move on from this unfortunate experience, I highly recommend their services to anyone who has been affected by online fraud Reach out to their Info below WhatsApp: 1 252378-7611 Website info; www.cyberconstableintelligence.com
30.03.25 03:25 orrinharber

The ad on X (formerly Twitter) popped up on my timeline one lazy Sunday afternoon. It was flashy, bold, and impossible to ignore. The headline read, “ONCE-IN-A-LIFETIME COMEDY EXTRAVAGANZA!” with a dazzling graphic of a stage lit up in neon lights. The caption said: “Kevin Hart, Ali Wong, Dave Chappelle, and a MYSTERY LEGENDARY COMEDIAN live in Vegas! Limited VIP tickets available. Don’t miss out!” The post had thousands of likes, retweets, and comments like, “This is going to be epic!” and “Already got my ticket!” It even had a blue checkmark next to the account name, which made it seem legit. I clicked the link, and it took me to a sleek website with a countdown timer and a list of sold-out ticket tiers. The only option left was a $125,000 VIP package, which promised front-row seats, backstage access, and a meet-and-greet with the comedians. I hesitated for a moment, but the fear of missing out got the better of me. I thought, When will I ever get a chance like this again? So, I entered my credit card details and hit “Purchase.” The confirmation email came through instantly, and I felt a rush of excitement. Little did I know, I’d just fallen for one of the most elaborate scams I’d ever encountered. Looking back, I should’ve noticed the red flags the overly pushy tone of the ad, the lack of reviews for the event, and the fact that no official accounts from the comedians promoted it. But in the moment, it all seemed so real.I had been swept up by the flashy ad, the excitement of the event, and the FOMO (fear of missing out) that made it seem like an opportunity I couldn’t let slip by. Everything about the ad screamed “exclusive” and “once-in-a-lifetime,” which was enough to convince me to take the plunge. Yet, as time went on and I tried to follow up on the event, I found there was no trace of it anywhere. There were no details, no event pages, and no mention from the comedians themselves. My heart sank as I realized I had been scammed.Thankfully, GRAYWARE TECH SERVICES helped me get my money back, but the experience was a hard lesson in online scams. I’ll never forget that ad on X the one that cost me $125,000 and a whole lot of pride. I learned the importance of being cautious online, checking for reviews, and looking for signs of authenticity before jumping into anything that seems too good to be true.You can reach GRAYWARE TECH SERVICES on web at ( https://graywaretechservices.com/ ) also on Mail: ([email protected])
30.03.25 06:22 grace111

I recommend Marie ([email protected] and WhatsApp: +1 7127594675) for recovering lost or stolen bitcoin, USDT, or any other cryptocurrency from fraudulent investment sites because they are very knowledgeable in the industry and will reimburse all of your money. I can say with confidence that she was the only one who was able to credit my account with $52,760 of the money that had gone missing, based on my prior transactions with them. She was the only one who could. Since they are the only ones that can fully return your missing funds to your account without any deductions, I sincerely appreciate their job and am recommending her to you today.
31.03.25 11:00 maryjul75

Fast Agent Recovery is a consulting firm that specializes in the recovery of assets from financial fraud. We know how to recover your funds and we have helped thousands of scam victims from around the world to recover their money. If you are a victim of Binary Options fraud, Forex fraud, Bitcoin fraud, Dating scams or one of the many other the online fraudulent practices that permeate the internet then file a complaint on www.fastrecoveryagent,.com to get an assessment if we can help you get your money back too. File a complaint: https://www.fastrecoveryagent.com/
02.04.25 23:16 frederickhandy

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02.04.25 23:18 frederickhandy

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03.04.25 07:57 messijohn

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03.04.25 10:58 elizabethrush89

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03.04.25 10:58 elizabethrush89

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04.04.25 09:49 mkihn634

When my $870,000 Bitcoin investment was trapped in the crumbling ruins of an offshore exchange, I felt as if my financial future had been buried at sea. It was meant to be my safety net, a shield against life’s uncertainties, but now it seemed like nothing more than a digital ghost. Each news update about the exchange's insolvency hit me like a tidal wave, dragging my hope under. It was a sleepless night, scanning through horror stories in forums as other people were losing everything. I could see my dreams flying out the window. That is until a former client, who had weathered a similar storm, said GRAYWARE TECH SERVICES in a hushed but confident tone. "They are the best there is," he said. "They were like detectives and lawyers.". Desperation drove me to call them, and in the initial discussion, I knew I was dealing with experts who had experienced everything. Their legal experts were well-versed with international financial rules like the back of their hands. They deciphered the network of shell corporations and offshore locations quicker than I could update my email. Their technical team, no less relentless, followed the transaction trails with precision akin to a surgeon. What impressed me most was their thoroughness. Every update came with legal documentation so polished it looked fit for a courtroom. They liaised with authorities across borders, cutting through red tape with the precision of a seasoned diplomat. When obstacles arose, and they did, the team adapted without breaking stride. Their persistence became my anchor. Exactly 27 days after my initial call, I received an email that made my heart skip. My Bitcoin had been recovered and safely transferred to my new secure wallet. I stared at the screen, tears mixing with disbelief and relief. GRAYWARE TECH SERVICES not only retrieved my money but also restored my faith in getting finances. They guided me through the entire process of protecting my assets in this unstable world known as the online space. I now sleep soundly knowing that an impenetrable system shields my backside, thanks to their assistance. Their legal acumen is as sharp as their technological prowess. I owe my financial future to their tireless work. They truly are the guardians of the digital age. You can reach them on web at ( https://graywaretechservices.com/ ) also on Mail: ([email protected]) whatsapp (+18582759508)
06.04.25 02:29 famousmullica

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06.04.25 11:30 maryjul75

As at September this year I got scammed by a fake investment broker, they took my savings, happiness, health, hope, trust and left me in tears and agony. My next thought was capitalized on suicide attempts, I also tried two different hackers who lured me to borrow for my colleagues but yet another scam. I saw a link on my Facebook group which I joined and luckily I came across www.fastrecoveryagent.com, they work in hand with the FBI in the united states. So I reported my case with evidence of payment made to the so-called investment broker, the ic3 investigator's carried out investigation to confirm if I was also tell the truth and after they must have concluded on my case I received back my money. I'm so grateful for your help today, words alone can't show how Happy and Alive I'm ever since I came across your great works. Thanks once more.
07.04.25 11:16 Lindaporche5

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07.04.25 11:16 Lindaporche5

They froze my $275K in Bitcoin. Blockchain cyber retrieve took actions. Running a startup in Nigeria is already a wild ride power cuts, red tape, and FX rates that dance like Afrobeats. But the day the government banned crypto transactions? Game over. My funds were locked in an exchange wallet. No access, No help I tried everything VPNs, New accounts Support bots. Nothing changed, Then someone in a Signal group dropped the name: BLOCKCHAIN CYBER RETRIEVE. These folks didn’t fight the exchange they outsmarted it. Peer-to-peer protocols. DeFi tools. Secure escrow networks. Nine days later, I got the email “Wallet restored. Check your balance. Every Single Satoshi. Was back. Since then? Fully decentralized, Unbothered, Unbanked. Whenever new laws try to stifle African innovation, I just sip palm wine and say: Let them try. We’ve got BLOCKCHAIN CYBER RETRIEVE now.” CONTACT THEM: Whatsapp +1, 5,2,0, 5,6,4, 8,3 0 0 Email: B L O C K C H A I N C Y B E R R E T R I E V E @ P O S T . C O M OR SUPPORT @ B L O C K C H A I N C Y B E R R E T R I E V E .O R G
07.04.25 18:09 rashmiramesh

I was introduced to crypto by my son a few years ago, I invested in USDT and BTC using Binance, I have several accounts including personal bank accounts so I was unable to keep up with all of them and ended up forgetting my secret codes used in accessing the account, I asked my son to help me since he had introduced me Crypto, unfortunately he gave me bad news that I had lost my investment, I was so heartbroken considering I had invested my life saving of $70,700, I narrated my ordeal to one of my friends who happened to know someone who had a similar experience, so after I met him he directed to where he got help, he told me that LEE ULTIMATE HACKER who were able to help him with his recovery problem, I quickly contacted them to help me with my lost funds, I was a bit skeptical about it coz of what I had gone through the last few days, the frustration and anxiety was getting to me, after contacting LEE ULTIMATE HACKER one of their team members took me through the recovery process explaining on how it works and what was required from my end ,he informed me that it would take 12 hours for my funds to be recovered, I was so anxious but they assured me that all will be well and soon enough I will be able to have full control of my wallet, true to their word LEE ULTIMATE HACKER team were able to recover my wallet and I was able to access and change my log ins to my wallet, I was so happy and I couldn’t believe it I logged in and out of my account a few times just to be sure, for any lost crypto contact LEE ULTIMATE HACKER via LEEULTIMATEHACKER @ AOL . COM telegram: LEEULTIMATE wh@tsapp +1 (715) 314 - 9248 the solution to all your recovery problems.
07.04.25 18:32 elizabethrush89

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07.04.25 18:32 elizabethrush89

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07.04.25 21:03 elizabethrush89

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08.04.25 03:00 natashaohnson

Working in conflict zones means improvising. When normal banking channels failed us, our NGO relied on Bitcoin to buy medical supplies directly. It worked, until a missile strike took out our field office, along with the hardware wallet that stored $410,000 in funds. Overnight, our ability to deliver life-saving aid is paralyzed. Amidst the chaos, I reached out to contacts in the humanitarian world. A UN aid worker whispered a name: Cyber Constable Intelligence. "They're the ones who can help you recover lost crypto," he assured me. Despair and hope clashed as I dialed their team on a satellite phone in a conflict zone. What followed was nothing short of a virtual rescue mission. Cyber Constable Intelligence's blockchain forensic experts didn't simply "recover" our assets; they improvised a fix like battlefield medics performing triage. They tracked our wallet's blockchain timestamps, reconstructing lost credentials from synced backups and transaction history. Working under direst duress, we communicated information between spotty internet and backup power sources. Cyber Constable Intelligence team members improvised, rendering security protocols impenetrable as they worked through the jurisdictional nightmares of working within war zones. Every update from them was a pulse that kept our mission alive. After our last available backup failed, they instituted a complex cryptographic reconstruction technique, a process I still don't understand, but it worked. Twelve days later, my satellite device displayed a message: "Access restored. Funds secured." It was not money. It was bandages, antibiotics, clean water, and hope. Thanks to Cyber Constable Intelligence, we replenish our medical supplies, ensuring that patients, innocent victims who had been caught in the crossfire, received the treatment they deserved. More than restoration, they advised us on decentralized storage and multi-signature security for long-term durability. We don't simply utilize Bitcoin presently; we utilize it astutely. Now, each time I sign a crypto transaction, I remember that minute, receiving life-saving medication that might not have come but for this group. In times of war, not every hero wears a uniform. Some carry keyboards, hunting down lost assets and securing humanitarian aid. Cyber Constable Intelligence not only restored our crypto, they kept our mission in the battle. If you think Bitcoin is just an investment, think again. To us, it's a lifeline. CYBER CONSTABLE INTELLIGENCE INFO: WhatsApp: 1 252378-7611 Website info; www.cyberconstableintelligence.com Email Info [email protected] Telegram Info: https://t.me/cyberconstable
10.04.25 23:26 patricialovick86

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10.04.25 23:27 patricialovick86

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11.04.25 00:48 patricialovick86

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11.04.25 03:20 edwardoelliott6

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11.04.25 08:10 Beatrice Gallagher

I was defrauded of $78,000 by an individual I met online who was involved in a fraudulent investment endeavour. I initiated a search for legal assistance to retrieve my funds, and I encountered numerous testimonies regarding a criminal named RecoveryHacker101. I contacted them and provided the requisite information. The experts were able to locate and assist in the recovery of my misappropriated funds within approximately 36 hours. The fraudster was apprehended and apprehended by local authorities in his region, which is a source of immense relief for me. I trust that this information will be beneficial to the numerous individuals who have fallen victim to these fraudulent online investment scams. Their professional services are highly recommended for those in need of prompt and effective recovery assistance. If you require their services, you may only contact them via email at RecoveryHacker101[at]gmail[dot]com.
13.04.25 01:13 michaeldavenport218

I was recently scammed out of $53,000 by a fraudulent Bitcoin investment scheme, which added significant stress to my already difficult health issues, as I was also facing cancer surgery expenses. Desperate to recover my funds, I spent hours researching and consulting other victims, which led me to discover the excellent reputation of Capital Crypto Recover, I came across a Google post It was only after spending many hours researching and asking other victims for advice that I discovered Capital Crypto Recovery’s stellar reputation. I decided to contact them because of their successful recovery record and encouraging client testimonials. I had no idea that this would be the pivotal moment in my fight against cryptocurrency theft. Thanks to their expert team, I was able to recover my lost cryptocurrency back. The process was intricate, but Capital Crypto Recovery's commitment to utilizing the latest technology ensured a successful outcome. I highly recommend their services to anyone who has fallen victim to cryptocurrency fraud. For assistance, contact [email protected] Capital Crypto Recover on Telegram OR Call Number +1 (336)390-6684 via email: [email protected]
13.04.25 01:13 michaeldavenport218

I was recently scammed out of $53,000 by a fraudulent Bitcoin investment scheme, which added significant stress to my already difficult health issues, as I was also facing cancer surgery expenses. Desperate to recover my funds, I spent hours researching and consulting other victims, which led me to discover the excellent reputation of Capital Crypto Recover, I came across a Google post It was only after spending many hours researching and asking other victims for advice that I discovered Capital Crypto Recovery’s stellar reputation. I decided to contact them because of their successful recovery record and encouraging client testimonials. I had no idea that this would be the pivotal moment in my fight against cryptocurrency theft. Thanks to their expert team, I was able to recover my lost cryptocurrency back. The process was intricate, but Capital Crypto Recovery's commitment to utilizing the latest technology ensured a successful outcome. I highly recommend their services to anyone who has fallen victim to cryptocurrency fraud. For assistance, contact [email protected] Capital Crypto Recover on Telegram OR Call Number +1 (336)390-6684 via email: [email protected]
14.04.25 23:44 elizabethrush89

God bless Capital Crypto Recover Services for the marvelous work you did in my life, I have learned the hard way that even the most sensible investors can fall victim to scams. When my USD was stolen, for anyone who has fallen victim to one of the bitcoin binary investment scams that are currently ongoing, I felt betrayal and upset. But then I was reading a post on site when I saw a testimony of Wendy Taylor online who recommended that Capital Crypto Recovery has helped her recover scammed funds within 24 hours. after reaching out to this cyber security firm that was able to help me recover my stolen digital assets and bitcoin. I’m genuinely blown away by their amazing service and professionalism. I never imagined I’d be able to get my money back until I complained to Capital Crypto Recovery Services about my difficulties and gave all of the necessary paperwork. I was astounded that it took them 12 hours to reclaim my stolen money back. Without a doubt, my USDT assets were successfully recovered from the scam platform, Thank you so much Sir, I strongly recommend Capital Crypto Recover for any of your bitcoin recovery, digital funds recovery, hacking, and cybersecurity concerns. You reach them Call/Text Number +1 (336)390-6684 His Email: [email protected] Contact Telegram: @Capitalcryptorecover His website: https://recovercapital.wixsite.com/capital-crypto-rec-1
17.04.25 03:56 tylerkevin

I discovered CheapCrypto net while searching for a cryptocurrency trading platform that promised lower fees and better arbitrage opportunities than the well-known Binance. Intrigued by the potential for profit, I decided to take a leap of faith and exchanged approximately $45,700.567 worth of USDC for Ethereum. Initially, everything seemed to be going smoothly, and I felt optimistic about my investment.However, when I attempted to transfer my newly acquired Ethereum to my main crypto wallet, I encountered a significant problem. The website repeatedly displayed a message saying, "Trying again…" but my funds remained stuck on CheapCrypto net. As the minutes turned into hours, panic set in. I began to realize that I might have fallen victim to scammers.Desperate for a solution, I started researching ways to recover my lost funds. That’s when I came across DUNAMIS CYBER SOLUTION, a service that specializes in helping individuals recover lost or stolen cryptocurrency. Their reputation for assisting victims of scams and fraudulent platforms gave me a glimmer of hope. I reached out to them, explaining my situation and the challenges I faced with CheapCrypto net.The team at DUNAMIS CYBER SOLUTION was incredibly responsive and professional. They guided me through the process of documenting my transaction and provided me with the necessary steps to initiate a recovery request. Their expertise in dealing with similar cases was evident, and I felt reassured that I was in capable hands.Within a short period, Y DUNAMIS CYBER SOLUTION began their investigation into CheapCrypto net. They utilized advanced tracking techniques to trace the flow of my funds and identify the scammers behind the platform. Their thorough approach and commitment to helping me recover my lost assets were impressive.After a few days of diligent work, I received the fantastic news that DUNAMIS CYBER SOLUTION had successfully traced my Ethereum and was able to facilitate its return. I was overjoyed to have my $45,700.567 restored, and I couldn’t be more grateful for the assistance I received.This has taught me a valuable lesson about the importance of conducting thorough research before engaging with new trading platforms. While the allure of lower fees and arbitrage opportunities can be tempting, it’s crucial to prioritize security and reliability. Thanks to DUNAMIS CYBER SOLUTION, I was able to recover my funds and regain my peace of mind. I promised them that after recovering my assets, I would spread the good news to others who faced similar challenges, ensuring they know there is hope and DUNAMIS CYBER SOLUTION are available 24/7. +13433030545 [email protected] [email protected]
18.04.25 20:39 michaeldavenport218

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18.04.25 20:39 michaeldavenport218

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21.04.25 12:19 michaeldavenport218

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03.05.25 05:01 melissaholroyd

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08.05.25 03:35 jwright70

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13.05.25 19:18 canemarcus0

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13.05.25 19:24 graceharrison09

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13.05.25 19:26 John Willis

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13.05.25 20:21 wendytaylor015

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13.05.25 20:21 wendytaylor015

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14.05.25 14:45 Clarkanderson

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14.05.25 14:45 Clarkanderson

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14.05.25 14:45 Clarkanderson

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16.05.25 14:18 patricialovick86

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16.05.25 14:18 patricialovick86

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17.05.25 18:33 marywilliam3544

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19.05.25 05:42 kylebro

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20.05.25 11:40 robertalfred175

CRYPTO SCAM RECOVERY SUCCESSFUL – A TESTIMONIAL OF LOST PASSWORD TO YOUR DIGITAL WALLET BACK. My name is Robert Alfred, Am from Australia. I’m sharing my experience in the hope that it helps others who have been victims of crypto scams. A few months ago, I fell victim to a fraudulent crypto investment scheme linked to a broker company. I had invested heavily during a time when Bitcoin prices were rising, thinking it was a good opportunity. Unfortunately, I was scammed out of $120,000 AUD and the broker denied me access to my digital wallet and assets. It was a devastating experience that caused many sleepless nights. Crypto scams are increasingly common and often involve fake trading platforms, phishing attacks, and misleading investment opportunities. In my desperation, a friend from the crypto community recommended Capital Crypto Recovery Service, known for helping victims recover lost or stolen funds. After doing some research and reading multiple positive reviews, I reached out to Capital Crypto Recovery. I provided all the necessary information—wallet addresses, transaction history, and communication logs. Their expert team responded immediately and began investigating. Using advanced blockchain tracking techniques, they were able to trace the stolen Dogecoin, identify the scammer’s wallet, and coordinate with relevant authorities to freeze the funds before they could be moved. Incredibly, within 24 hours, Capital Crypto Recovery successfully recovered the majority of my stolen crypto assets. I was beyond relieved and truly grateful. Their professionalism, transparency, and constant communication throughout the process gave me hope during a very difficult time. If you’ve been a victim of a crypto scam, I highly recommend them with full confidence contacting: Email: [email protected] Telegram: @Capitalcryptorecover Call/Text: +1 (336) 390-6684 Website: https://recovercapital.wixsite.com/capital-crypto-rec-1
20.05.25 11:40 robertalfred175

CRYPTO SCAM RECOVERY SUCCESSFUL – A TESTIMONIAL OF LOST PASSWORD TO YOUR DIGITAL WALLET BACK. My name is Robert Alfred, Am from Australia. I’m sharing my experience in the hope that it helps others who have been victims of crypto scams. A few months ago, I fell victim to a fraudulent crypto investment scheme linked to a broker company. I had invested heavily during a time when Bitcoin prices were rising, thinking it was a good opportunity. Unfortunately, I was scammed out of $120,000 AUD and the broker denied me access to my digital wallet and assets. It was a devastating experience that caused many sleepless nights. Crypto scams are increasingly common and often involve fake trading platforms, phishing attacks, and misleading investment opportunities. In my desperation, a friend from the crypto community recommended Capital Crypto Recovery Service, known for helping victims recover lost or stolen funds. After doing some research and reading multiple positive reviews, I reached out to Capital Crypto Recovery. I provided all the necessary information—wallet addresses, transaction history, and communication logs. Their expert team responded immediately and began investigating. Using advanced blockchain tracking techniques, they were able to trace the stolen Dogecoin, identify the scammer’s wallet, and coordinate with relevant authorities to freeze the funds before they could be moved. Incredibly, within 24 hours, Capital Crypto Recovery successfully recovered the majority of my stolen crypto assets. I was beyond relieved and truly grateful. Their professionalism, transparency, and constant communication throughout the process gave me hope during a very difficult time. If you’ve been a victim of a crypto scam, I highly recommend them with full confidence contacting: Email: [email protected] Telegram: @Capitalcryptorecover Call/Text: +1 (336) 390-6684 Website: https://recovercapital.wixsite.com/capital-crypto-rec-1
26.05.25 17:55 Juliuslaurence1

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