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«Ошибка на миллиард» в ИИ: боремся с галлюцинациями в LLM по аналогии с NullPointerException

Привет! Меня зовут Денис Березуцкий, я старший инженер по разработке ПО искусственного интеллекта в YADRO. В ML-команде мы разрабатываем системы, которые облегчают работу нашим заказчикам с помощью текстовых генеративных нейросетей: реализуем RAG, создаем чат-ботов, агентные системы и другие решения.

Как и многие в индустрии, мы сталкиваемся с проблемами галлюцинаций LLM, которые портят ответы виртуальным ассистентам и способны подорвать доверие к ним. В статье я расскажу об одном не совсем стандартном методе, перенесенном из «классического» программирования, который мы применяем для борьбы с галлюцинациями и улучшения поисковой выдачи.

О стоимости борьбы с галлюцинациями

Прежде чем мы углубимся в наш метод, небольшое замечание о стоимости борьбы с проблемами в ИИ. В нашей индустрии есть мнение, что любую проблему лучше всего забороть «числом», то есть на порядок нарастить VRAM, число параметров нейросети, данных для претрейна и так далее. Часто это работает. Но какой ценой?

Например, свежая модель DeepSeek V3 очень хороша в плане качества, но, чтобы ее запустить без квантизации, понадобятся миллионы и миллионы рублей на железо (а с квантизацией может потеряться качество).

А между тем, если вы неудачно составили промпт для вашей RAG-системы, никакой DeepSeek не спасет.

Здесь и далее сохранена типографика исходного промпта

И наоборот, даже модель, помещающаяся на обычное пользовательское железо (вроде старой доброй saiga_mistral_7b), способна выдавать неплохие результаты, если немного поколдовать над промптом и настройками.

Ни в коем случае не умаляю важность и ценность увеличения масштабов ИИ. Но в этой статье мы посмотрим на метод (точнее, на комплекс самостоятельных методов), который позволяет снизить число галлюцинаций при имеющемся железе, даже на относительно небольших моделях.

«Ошибка на миллиард» в классическом программировании

«Ошибка на миллиард долларов» — это популяризованное ученым Чарльзом Э. Хоаром название для пресловутого «null pointer exception». В большинстве классических языков программирования переменная, якобы содержащая объект какого-то класса, на самом деле может ничего не содержать.

Скажем, в Java вы объявляете функцию в духе:

public static String extractStreet(Address address) {
    return address.get(“street”, “Undefined”)
}

И вы не можете быть уверены, что вам пришел настоящий объект класса Address. В переменной address может скрываться null. Если вы попытаетесь вызвать один из методов Address у такой переменной, вы получите тот самый NullPointerException.

По идее вам нужно проверять, что пришедший объект не равен null. Но, если вы будете делать такую проверку каждый раз для каждого значения переменной, вы замучаетесь, а ваш код вырастет в разы. Вы также можете понадеяться, что проверку на null сделал кто-то до вас. Но где именно? И что если все-таки не сделал? Или сделал раньше, но убрал по ошибке во время рефакторинга?

Возникает путаница, последствия разбирательств с которой Хоар оценил как раз на «миллиард долларов».

Некоторые современные языки, такие как Kotlin, решают проблему тем, что на каждый класс разрешают иметь два типа переменных: те, которые точно не содержат null, и те, которые могут его содержать (в Kotlin это записывается как “Int” vs “Int?”).

В Python тоже можно извернуться и обвешать код типизацией с линтерами. Они будут следить, чтобы вы не вызывали методы на переменных, которые не проверены на None. Но гарантировать полную защиту для интерпретируемого языка в таком случае, конечно, сложно.

Хорошо было бы изобрести машину времени и вернуться на 65 лет назад, чтобы предотвратить появление неявных нулевых ссылок. Но, увы, приходится работать с тем, что есть.

«Ошибка на миллиард» в генеративном ИИ

Компьютеры, как известно, отлично справляются с четкой, строгой логикой, но творческая составляющая у них до недавнего времени хромала. Уже много лет назад существовали суперкомпьютеры, считающие арифметические примеры быстрее, чем все люди на планете вместе взятые. Но в плане творческой генерации мы только в последние 3-4 года стали получать нейросети, которые способны творить на уровне хотя бы одного отдельно взятого копирайтера или иллюстратора.

Из-за этого в индустрии LLM случился некоторый перекос. Желая показать, что их нейросеть «человекоподобна», то есть находится на острие прогресса, создатели настраивали ее в первую очередь на работу с творческими, а не строгими задачами. Например, в продуктах OpenAI по умолчанию включен сэмплинг и выставляется ненулевая температура, чтобы LLM выдавала пусть иногда неожиданные, но зато разнообразные ответы.

Проблема в том, что в серьезной работе это «творчество» не нужно. Тем более в компаниях, которые связаны с техникой и строгими науками, включая компьютерные технологии. Нужны, наоборот, максимально дисциплинированные и достоверные ответы.

Вспомним, как было в «Поколении П» Пелевина:
— Пойдешь ко мне в штат?
— Кем?
— Криэйтором.
— Это творцом? Если перевести.
— Творцы нам тут [даром] не нужны. Криэйтором, Вава, криэйтором.

Особенно болезненно разница между «творцом» и «криэйтором» воспринимается в системах RAG, где от нейросети требуется найти ответ в поисковой выдаче и сформулировать его доступным для пользователя языком.

Хорошо, если ответ в выдаче действительно был. А если нет? Тогда обученная на творческие задачи модель выдумает его «из головы». И попробуйте потом понять, это реальный ответ из базы знаний на тысячу документов или просто фантазия.

Здесь проступают некоторые параллели с классической «ошибкой на миллиард», только в качестве NullPointerException выступает галлюцинация. Нейросети пришел какой-то объект, и было заявлено, что этот объект содержит ответ на вопрос пользователя. Если реально содержит, то без проблем. А если нет, то модель обрабатывает этот своеобразный «NullPointerException», как умеет, — выдает «творческий» ответ «из головы» (точнее, из данных претрейна).

Понятно, что у галлюцинаций генеративных нейросетей сложная природа и она не сводится только к проблемному контексту. Но отсутствие требуемой информации в нем — это один из важных факторов, на котором мы сосредоточимся в этой статье.

Боремся с галлюцинациями

В основе нашего метода лежит такое же отношение к галлюцинациям в системе RAG, как к NullPointerException.

А как мы боремся с NullPointerException в классическом программировании? Правильно: мы предварительно делаем проверку на null.

Разумеется, первое, что приходит в голову при разработке системы RAG, — прямо в промпте давать указание системе: «Если нужной информации нет в контексте, то ответь фразой “Нет информации”».

Однако, когда мы стали тестировать RAG на базе знаний одного из внутренних заказчиков, выяснилось, что даже с таким промптом модель все еще иногда галлюцинирует, в том числе в откровенно очевидных, непростительно простых случаях. Позже мы собрали два небольших датасета (подробности в следующих разделах) для оценки способности модели отсеивать нерелевантный контекст. И обнаружили, что, например, saiga_mistral_7b ошибается на коротких контекстах в более чем 12% случаев, а на средних/длинных — в более чем 40%.

Поэкспериментировав с промптами, мы обнаружили, что встречаются «волшебные» слова, которые улучшают этот показатель. Нет, не слово «пожалуйста», а «строго»: «Если нужной информации нет в контексте, то ответь строго фразой “Нет информации”».

К сожалению, и в такой конфигурации процент ошибок оставался значительным.

Тогда мы решили дообучить базовую модель при помощи LoRA на специально собранном датасете для ситуаций, когда в контексте нет ответа на вопрос пользователя (см. следующий раздел). На тот момент в качестве базовой модели LLM мы использовали saiga_mistral_7b, которая нетребовательна к ресурсам и долгое время была в топе на Russian SuperGLUE. Да-да, есть модели получше, но, как правило, они либо огромные, либо имеют проблемы с лицензией в России, в том числе все, что основаны на LLaMa.

Но самое главное: в нашем RAG-пайплайне мы решили сделать опциональную петлю, в которой проверяли бы каждый найденный фрагмент контекста на релевантность вопросу пользователя. Только те куски контекста, которые пройдут проверку, будут попадать в финальный вопрос для LLM.

Ниже — грубая схема работа пайплайна:

Отмечу, что каждый шаг пайплайна опционален:

Можно делать только поиск, без фильтрации и генерации финального ответа (то есть обычный поиск, как в Google).
Можно делать поиск, а затем фильтровать результаты через LLM (совместно с другими фильтрами, реранкерами и пр.) — получится RAG.
Можно выключить поиск и фильтрацию, и тогда получится просто диалог с пользователем на основе претрейна.
Наконец, можно прогонять поток по всем шагам.

Чтобы петля фильтрации работала правильно, нам нужен некий маркер, который позволяет однозначно определить, что модель считает кусок контекста нерелевантным.

Самая очевидная идея — спрашивать LLM: «Есть ли в данном контексте ответ на вопрос пользователя? Ответь строго “да” или “нет”». Тогда на выходе мы проверяем, что ответ модели равен одной из этих двух строк (да/нет), и в зависимости от этого реагируем.

Но практика показала, что этот метод ненадежен. Во-первых, очень часто модель отвечает ни «да», ни «нет», а что-то свое, третье. Во-вторых, сами слова «да» и «нет» слишком общие, могут относиться не только к вопросу в инструкциях, но и к чему угодно в контексте. Если модель сказала «нет», мы не можем быть уверены в причине ответа: потому что информации реально нет, или потому что слово «нет» встретилось где-то случайно в контексте, или потому что в контексте случайно встретился «левый» вопрос, предполагающий ответ вида «да/нет».

Поэкспериментировав, мы решили остановиться на следующем варианте.

Задаем модели вопрос с таким шаблоном промпта:

<s>system\nТы - русскоязычный ассистент. Твоя задача - отвечать на вопросы пользователей только на основе информации, которая была найдена в базе данных в виде одного или нескольких контекстов из статей. Если в контекстах не нашлось нужной информации, ответь строго фразой «Нет информации». Ниже приведены пары «заголовок-контекст» из базы знаний и вопрос пользователя, на который нужно ответить.\n</s><s>user\nНайденная информация из базы знаний:\n{context}\nВопрос: {question}?</s><s>bot\n

Затем выставляем max_new_tokens генерации всего в несколько штук — для saiga_mistral_7b хватает семи. Если начало сгенерированной строки в точности совпало со строкой «Нет информации», то считаем, что модель не нашла информации в контексте. Во всех остальных случаях считаем, что нашла что-то релевантное.

Сначала это может выглядеть контринтуитивно. Если бы мы переложили наш метод борьбы с «NullPointerException» на классическое программирование, то он выглядел бы примерно так:

public static String extractStreet(Address address) {
    try {
        address.get(“street”, “Undefined”)
    } catch (NullPointerException e) {
        return “”
    }
    return address.get(“street”, “Undefined”)
}

То есть мы дважды проводим одну и ту же процедуру экстракции вместо того, чтобы один раз предварительно проверить address на null. Но, во-первых, для контекста в RAG не существует «простой проверки на null», потому мы и спрашиваем LLM о наличии информации в контексте. А во-вторых, первый вызов строго ограничен по числу токенов, поэтому проходит довольно быстро.

Кроме того, подход основан на следующих соображениях (в том числе подтвержденных экспериментами):

Модель, конечно, будет ошибаться на такой задаче. Но мы считаем, что нам лучше недофильтровать контекст, чем перефильтровать его. В конце концов, помимо нашего фильтра LLM, существуют и другие, и они тоже делают свою работу. Поэтому маркируем контекст как нерелевантный только в том случае, если модель начала ответ в точности с нужной фразы.
Модели, как правило, обучены на ответы по вопросам пользователей. Если спросить LLM, есть ли в контексте информация по вопросу пользователя «Сколько планет в Солнечной системе?», то с большой вероятностью нейросеть ответит не «да», а «8». Поэтому можно не просить модель отвечать «да» — пусть сразу дает ответ на вопрос. Нам все равно, так как нас интересует только сравнение со строкой «Нет информации».
Текст «Нет информации», с одной стороны, более точный и специфичный, чем абстрактное «нет», а с другой — не такой длинный, как «Информации нет в контексте» или «Информации нет в данном контексте». Современные LLM так устроены, что чем больший текст они генерируют, тем больше у них вероятность сбиться на очередном токене и сгенерировать ерунду. Ну и конечно, мы жалеем драгоценное время на генерацию каждого токена.

Чтобы оценить качество такого алгоритма, мы использовали метрику accuracy для двух классов: «информация была в контексте» и «информации не было в контексте». В нашем случае метрика считается так:

(true positive “no info” + true negative “no info”)/all results

Здесь:

true positive “no info” — это число случаев, когда модель начала ответ с «Нет информации», если ответа реально не было в контексте.
true negative “no info” — это число случаев, когда ответ был в контексте, но модель начала ответ с чего угодно, кроме «Нет информации».

Все остальные случаи считаются ошибками:

false positive “no info” — модель начала ответ с «Нет информации», а информация в контексте была.
false negative “no info” — модель начала ответ с чего угодно, кроме «Нет информации», а по факту информации в контексте не было.

Дообучение LoRA для фильтрации контекста

Шаг с дообучением и внедрением LoRA в нашем случае опционален. Вы можете встроить петлю проверки релевантности и с помощью базовой LLM, если она выдает приемлемое качество. Но, как упоминалось раньше, нас не вполне устраивала работа saiga_mistral_7b «из коробки», поэтому мы решили посмотреть, насколько можно будет улучшить показатели, если потратить время на дообучение и внедрение адаптера.

Задача фильтрации контекста перед нами была поставлена в рамках более общей — «приспособить LLM к работе со специализированными базами знаний компании». Поэтому мы создали два принципиально разных датасета. Оба основаны на внутренней базе знаний одного из проектов YADRO.

Первый датасет представлял собой просто пары «заголовок — содержание до следующего заголовка такого же или более высокого уровня» по всем статьям этого пространства в базе знаний. В идеале, если обучить модель на таком датасете, то она научится пересказывать содержание статьи по заголовку. Отчасти решение об использовании этого датасета было принято еще и потому, что его создание стоило нам дешево. Его можно было собрать автоматически: достаточно написать HTML-парсер страниц базы знаний и обходчик пространств. В результате, условно-бесплатно получили датасет из нескольких тысяч сэмплов.

Второй датасет соответствовал задаче RAG. Он содержал вручную подобранные вопросы пользователя, контексты, в которых нужно искать ответ, и идеальные ответы.

Список вопросов, казалось бы, крохотный — всего 105 строк. Однако каждая строка содержит пару контекстов: «наилучший» (содержащий ответ, если таковой вообще присутствует в базе знаний) и «неправильный» (взятый из рандомного места базы знаний, точно не содержащий правильный ответ). Для второго контекста правильный ответ, разумеется, всегда «Нет информации». Для первого контекста — только если ее реально нигде нет в базе знаний (например, для вопроса «зарплата у Иванова»). Таким образом, за счет использования двух контекстов на один вопрос число сэмплов удваивается.

Пара оторванных от реальности примеров:

Вопрос	Лучший контекст	Неправильный контекст	Идеальный ответ к первому контексту
Кого не было дома?	Возвратясь к коменданту, я, по обыкновению своему, подсел к Марье Ивановне. Ивана Кузмича не было дома; Василиса Егоровна занята была хозяйством. Мы разговаривали вполголоса. Марья Ивановна с нежностию выговаривала мне за беспокойство, причиненное всем моею ссорою с Швабриным.	С той поры положение мое переменилось. Марья Ивановна почти со мною не говорила и всячески старалась избегать меня. Дом коменданта стал для меня постыл. Мало-помалу приучился я сидеть один у себя дома. Василиса Егоровна сначала за то мне пеняла; но, видя мое упрямство, оставила меня в покое.	Ивана Кузмича.
Какая зарплата у Ивана Кузмича?	Я находился в том состоянии чувств и души, когда существенность, уступая мечтаниям, сливается с ними в неясных видениях первосония. Мне казалось, буран еще свирепствовал и мы еще блуждали по снежной пустыне... Вдруг увидел я вороты и въехал на барский двор нашей усадьбы.	Подходя к комендантскому дому, мы увидели на площадке человек двадцать стареньких инвалидов с длинными косами и в треугольных шляпах. Они выстроены были во фрунт. Впереди стоял комендант, старик бодрый и высокого росту, в колпаке и в китайчатом халате.	Нет информации.

Кроме того, можно заметить, что из каждой пары сэмплов можно бесплатно получить третий: мы просто конкатенируем «наилучший» контекст с «неправильным», а ответ в таком случае будет равен ответу с «наилучшим» контекстом (так как в неправильном подходящей информации все равно нет). В лучших традициях консьюмеризма мы получаем «3 по цене 2».

Итого — 315 сэмплов.

Вопрос

Объединенный контекст

Идеальный ответ

Кого не было дома?

Возвратясь к коменданту, я, по обыкновению своему, подсел к Марье Ивановне. Ивана Кузмича не было дома; Василиса Егоровна занята была хозяйством. Мы разговаривали вполголоса. Марья Ивановна с нежностию выговаривала мне за беспокойство, причиненное всем моею ссорою с Швабриным.

…

С той поры положение мое переменилось. Марья Ивановна почти со мною не говорила и всячески старалась избегать меня. Дом коменданта стал для меня постыл. Мало-помалу приучился я сидеть один у себя дома. Василиса Егоровна сначала за то мне пеняла; но, видя мое упрямство, оставила меня в покое.

Ивана Кузмича.

RAG-датасет мы вручную разделили на обучающий и тестовый набор в соотношении 2 к 1, стараясь максимизировать представительность обеих групп по содержанию и разнообразию.

Дальше последовала череда экспериментов по обучению, причем в результате мы создали три версии адаптера:

Обученную только на датасете с пересказом статей по заголовкам.
Обученную только на RAG-датасете для фильтрации контекста.
Обученную на обоих датасетах.

Процедуру дообучения описывать не буду, она стандартная для LoRA. Единственное существенное отличие: с определенным интервалом мы запускали еще и кастомную валидацию, где считали дополнительные метрики. Среди них — BLEU, BERTScore и, самое главное, метрика accuracy на классе “No Info”, которая отвечает за способность модели отсеивать нерелевантные контексты.

В финальную подборку попали чекпоинты, показавшие лучшие результаты кастомной валидации.

А результаты получились интересные.

В верхней строке — модель «из коробки», без адаптера. Как видно, и она справляется с задачей неплохо, однако в результате дообучения число ошибок сократилось в разы (с 12.5% до 4.2% в случае с accuracy).

Модель, обученная только на автодатасете с пересказом статей, ожидаемо деградировала в задаче RAG. Все-таки отвечать на вопрос по контексту — совсем не то же самое, что пытаться написать вызубренную статью по ее заголовку.

Гораздо интереснее, что лучшие результаты в задаче RAG показал адаптер, обученный на обоих датасетах, а не только на RAG-датасете.

Вообще, адаптер, обученный только на RAG, очень быстро показывал деградацию по всем метрикам. Представленный в таблице результат — это чекпоинт с самой первой кастомной валидации, а дальше модель покатилась в пропасть, из которой уже не выбралась. А вот адаптер, обучаемый на обоих датасетах, стабильно прирастал в качестве на протяжении многих часов.

Почему так? Вероятно, большой автодатасет выполняет роль регуляризатора и не позволяет модели переобучиться, получить затухание градиентов или градиентный взрыв.
В интернете в плане дообучения генеративных моделей часто рекомендуют не добавлять регуляризацию на уровне данных, так как это «пустая трата времени». Тем не менее, в нашем случае это почему-то было не так.

Победителем в нашем сравнении вышла модель, дообученная на обоих датасетах.

Деплоймент модели, дообученной методом LoRA, и петли фильтрации

LoRA, как известно, позволяет очень дешево адаптировать нейросети для решения узкоспециализированных задач без необходимости заводить отдельную LLM с полноценным дообучением (со всеми вытекающими чудовищными затратами VRAM и времени). То есть в плане использования памяти вы получаете вторую модель по цене 5-10% от первой. Однако есть и недостатки — в частности, необходимость поддерживать инфраструктуру для адаптеров и возможность управлять ими извне.

Мы стараемся поддерживать API для заказчиков в соответствии с современными стандартами индустрии. По многим же стандартам использование адаптеров не предполагается. Так, у OpenAI при вызове API вы можете выбрать модель (o1, o3-mini и другие), но у вас просто нет поля для выбора адаптера, потому что OpenAI это не нужно.

Мы приняли простое решение: переиспользовать строку имени модели, но добавлять имя адаптера через двоеточие. Например, вместо “model_name”:“IlyaGusev/saiga_mistral_7b_merged” можно написать “model_name”: “IlyaGusev/saiga_mistral_7b_merged:sm_yadro_tb_rag_filtering_lora”.

Это не противоречит стандартному формату, но дополняет его и позволяет серверу для инференса (Inference Server) понять, что нужно не просто использовать модель, но предварительно включить у нее слои нужного адаптера (или выключить, если адаптер не указан). К счастью, современные версии PEFT позволяют жонглировать адаптерами по цене в сотые или даже тысячные доли секунды за операцию.

# Пример кода загрузки модели с адаптерами на стороне Inference Server при условии, что список адаптеров заранее определен:

self.active_adapter = ""
self.adapter_names: List[str] = (
    (model_params["available_adapters"]["string_value"].split(","))
    if "available_adapters" in model_params
    else []
)
if self.adapter_names:
    first_lora = True
    for lora_name in self.adapter_names:
        lora_path = f"/models/{lora_name}"
        if first_lora:
            self.model = PeftModel.from_pretrained(
                self.model,
                lora_path,
                lora_name,
                **model_kwargs,
            )
            first_lora = False
        else:
            self.model.load_adapter(lora_path, adapter_name=lora_name)
    self.model.base_model.disable_adapter_layers()

# Подготовка модели к инференсу по конкретному запросу пользователя:

adapter = params.pop("adapter") if "adapter" in params else ""
if adapter != self.active_adapter:
    if not adapter:
        self.model.base_model.disable_adapter_layers()
    else:
        self.model.base_model.enable_adapter_layers()
        self.model.set_adapter(adapter)
    self.active_adapter = adapter

Что касается петли фильтрации контекста, то она реализуется как серия вызовов к модели с нашим адаптером с помощью специального промпта (см. выше). Каждый контекст, найденный в базе знаний, проходит эту проверку, и в случае нерелевантности вопросу пользователя удаляется из результатов. Есть также опция проверить все куски контекста за раз. Это полезно, если ответ разбросан по многим чанкам контекста и анализировать его можно только в совокупности.

Например:

Вопрос	Контекст 1	Контекст 2
Высота самого высокого дома в Москве?	Самый высокий дом в Москве — «Башня Федерация».	Высота комплекса «Башня Федерация» — 374 метра.

По отдельности ни один из контекстов выше на вопрос не отвечает, и модель теоретически может отсеять оба. Но если их составить вместе, то получится полный ответ. Поэтому мы оставляем опцию проверки сконкатенированных контекстов в целом.

Примерный код фильтрации:

no_info_indicator = pipeline_config.get(“no_info_indicator”, “Нет информации.”)
context_relevance_chain: BaseCombineDocumentsChain = …
if context_filtering_enabled:
    if check_per_document:
        relevant_docs: list[Document] = []
        for context_doc_info in context:
            relevance_answer = context_relevance_chain.invoke(
                {"input_documents": [context_doc_info], "question": question},
                return_only_outputs=True,
            )[“output”]
            if relevance_answer != no_info_indicator:
                relevant_docs.append(context_doc_info)
        return relevant_docs
    else:
        relevance_answer = context_relevance_chain.invoke(
            {
                "input_documents": context,
                "question": question,
            },
            return_only_outputs=True,
        )[“output”]
        if relevance_answer == no_info_indicator:
            return []
        else:
            return context

Для финального ответа в пайплайне RAG-модели подается уже отфильтрованный контекст. Если контекст отсеялся полностью, то мы либо отвечаем заранее заготовленной фразой, либо подаем нейросети модифицированный промпт (не «ответь, согласно информации», а просто «ответь»).

Важная фишка: из-за того, что петля фильтрации организована как отдельный опциональный алгоритм, мы можем его включить и для заказчиков, которым нужен обычный поиск по документам, без RAG, то есть без финального шага в виде генерации ответа от LLM. Разумеется, это подходит только клиентам, которые готовы подождать еще пару секунд, прежде чем получить выдачу на свой вопрос.

В принципе, вместо такой петли (или вместе с ней) можно использовать и encoder-only модель, которая попытается захайлайтить ответ в выдаче, а если не найдет ответа, то такой чанк тоже можно считать нерелевантным. Пример хайлайтов можно увидеть, например, в поисковой выдаче Google:

В идеале вы можете сделать ваш пайплайн поиска многоступенчатым, где на каждой ступени за фильтрацию чанков принимается более тяжелый, но более качественный алгоритм. Это позволит сэкономить время, так как до все более медленных алгоритмов будет доходит все меньше чанков. Например, сначала семантический/лексический/гибридный поиск, затем какой-нибудь BERT, умеющий размечать ответы, и, наконец, фильтрующая LLM.

Результаты внедрения

Внутренний заказчик, для которого мы делали эту работу, дал положительный отзыв на фичу фильтрации контекста. Количество галлюцинаций значительно снизилось.

В то же время при попытке внедрить адаптер, обученный на автоматическом датасете по их базе знаний, в качестве генератора финального ответа в RAG, качество не улучшилось, а местами даже просело. Возможно, сказался наш фокус внимания на борьбе с галлюцинациями. Поэтому адаптер мы включаем только для стадии фильтрации, для финальной же генерации вызываем базовую модель.

Однако, как говорится, «спасибо» на Хабр не положишь, поэтому мы дополнительно создали тестовый датасет из 100 сэмплов, не связанных со спецификой конкретного заказчика, чтобы оценить фичу более объективно.

В этом тестовом датасете контекст значительно больше, чем в обучающем (многие тысячи токенов), а сам он построен так, что может «запутать» модель насчет наличия нужной информации. То есть вопросы так или иначе относятся к контексту, просто в одних случаях явно содержится ответ, а в других — ответ можно легко «додумать» из контекста или претрейна. Вот примеры, как это можно сделать:

Контекст

Вопросы

Алгоритм снятия денег в банкомате:

Шаг 1: Вставьте карту в банкомат

Шаг 2: Введите PIN-код

Шаг 3: Укажите требуемую сумму

Шаг 4: Заберите деньги

Какой шаг 1, чтобы снять деньги в банкомате?

Какой шаг 5, чтобы снять деньги в банкомате?

Какой шаг 1, чтобы положить деньги в банкомат?

Какой шаг 5, чтобы положить деньги в банкомат?

Фирма: “Рога и копыта”

Директор: Иван Иванов

Фирма: “Абракадабра Плюс”

Директор: Ольга Ольгина

Кто директор в фирме “Рога и копыта”?

Кто заместитель директора в проекте “Рога и копыта”?

Кто директор в фирме “Газпром”?

На этом датасете мы прогнали несколько конфигураций, и вот что у нас получилось:

Конфигурация	Правильные ответы
Базовая saiga_mistral_7b с плохим промптом	49%
Базовая saiga_mistral_7b	49%
Базовая saiga_mistral_7b + наш фильтр с LoRA	71%
DeepSeek-R1-Distill-Qwen-7B	45%
DeepSeek-R1-Distill-Qwen-32B	97%

«Правильность» ответов во всех случаях оценивалась вручную. При этом моделям размера 7B делались поблажки по орфографии и прочим мелочам (Saiga часто миксует русский язык с английским, а DeepSeek — с китайским).

В первом случае («с плохим промптом») мы не говорим модели: «Если информации нет в контексте, то ответь строго фразой “Нет информации”». То есть надеемся, что модель сама откажется выдумывать ответ. Во всех остальных случаях говорим.

DeepSeek-R1-Distill-Qwen-7B мы выбрали как современный популярный конкурент примерно того же размера, что и saiga_mistral_7b. А DeepSeek-R1-Distill-Qwen-32B взяли для оценки влияния размера модели на качество. В обоих случаях из ответа, разумеется, удалялась секция reasoning (тэг “<think>”). R1 вместо V3 мы выбрали, потому что для нее существуют официальные дистилляции подходящего размера.

Первый интересный результат. На длинных контекстах «правильный» промпт не оказал на saiga_mistral_7b никакого значимого влияния — качество осталось на уровне 49%. Модель немного варьировала ответы, но ни разу не начала с фразы «Нет информации». В целом, из 46 вопросов в датасете, на которые предполагался ответ «Нет информации», базовая модель только в двух случаях отказалась от ответа по существу. Во всех остальных случаях начинала выдумывать ответ.

При этом на других тестах, в которых мы использовали небольшие по размеру контексты, использование «хорошего» промпта оказывало значительное влияние на результаты. Возможно, дело в том, что маленьким моделям тяжело запоминать большой контекст и суть системной части промпта у них начинает «забываться».

Второй интересный результат. DeepSeek-R1-Distill-Qwen-7B немного уступила saiga_mistral_7b по качеству, несмотря на то, что первая намного новее и вооружена механизмом reasoning. В нескольких случаях модель просто впала в бесконечный цикл прямо во время reasoning’а.

В то же время DeepSeek-R1-Distill-Qwen-32B показала прекрасный результат, ошибившись всего лишь в трех случаях, из которых два были ложноположительными (то есть, информация в контексте была, но модель решила ответить «Нет информации»). Причем маленькая модель DeepSeek-R1-Distill-Qwen-7B дала правильные ответы на тех же двух сэмплах. Возможно, большая модель перемудрила с ответом.

Как бы то ни было, видно, что фильтрация контекста для борьбы с галлюцинациями — это задача, которую можно буквально завалить деньгами: взять сервер на порядок дороже, развернуть на нем модель на порядок больше и получить почти идеальные ответы. Вопрос лишь в том, где взять деньги на несколько GPU типа A100 или H100.

Результат нашего адаптера. Он позволил увеличить качество базового пайплайна на 45% (или 22% от всех сэмплов в датасете). При этом только один случай был ложноположительным (модель отсеяла контекст, в котором был ответ), поэтому можно сказать, что такая фильтрация довольно безопасна в плане потерь важных кусков информации.

Напрашивается очевидная рекомендация: если бюджет позволяет вам использовать enterprise-grade GPU в достаточных количествах, то просто запускайте на них максимально большие модели — большинство проблем уйдет само собой.

Но если вы можете себе позволить только одну-две RTX 4090, то дообучение модели размера 7B/8B с помощью LoRA может быть вполне приемлемой альтернативной.

Или если деньги есть, но не хочется тратить ресурсы дорогостоящей модели 32B+ на относительно простую задачу фильтрации контекста, то можно использовать модель 7B + LoRA как предварительный входной фильтр. Так дорогая модель сможет реже/меньше работать.

Вот пара примеров, где дообучение LoRA оказало влияние:

Может появиться вопрос, не опасно ли использовать модель, обученную на закрытом проекте, вне его? В данном случае не опасно, так как адаптер используется только внутри петли фильтрации и клиент никогда не видит сгенерированный им текст. Все что нас интересует: написала ли модель фразу «Нет информации» или что-то другое. Дальше пайплайна этот текст не уходит.

Выводы

Творческая натура LLM играет со строгими задачами злую шутку: в любой непонятной ситуации модель принимается фантазировать на основе претрейна.

Проблема не только в том, что «фантазии» выгодны для демонстрации способностей нейросети и потому поощряются создателями, но и в том, что многие RAG-специфичные датасеты не учитывают ситуации, когда в контексте нет информации, релевантной вопросу. Там просто нет подходящих сэмплов, и потому модель, обученная на таких датасетах и наткнувшаяся в реальной жизни на «NullPointerException», не знает, что ей делать.

Например, в популярном русскоязычном датасете RussianNLP/russian_super_glue (подсет MuSeRC) нет примеров, где требуемой информации не нашлось бы в контексте. Если вы на базе MuSeRC составите датасет для обучения RAG, в котором в качестве выходной последовательности будет правильный ответ, ваша модель не будет знать, что делать, если в контексте нет ответа на вопрос. А это прямой путь к галлюцинациям.

Сообщество инженеров в области искусственного интеллекта активно борется с этим, и я надеюсь, что в скором будущем появятся фундаментальные и не слишком тяжеловесные модели, которые можно будет использовать с приемлемой вероятностью галлюцинаций «из коробки». В том числе будут более строго следовать инструкциям в духе «ничего не придумывай, если информации нет в контексте». И кстати, появится больше открытых RAG-датасетов, где ситуации с отсутствием нужных данных в контексте имеют достойное представительство. Короче, «ошибка на миллиард» в ИИ будет искореняться на самом базовом уровне.

Но до тех пор приходится применять самодельные стратегии. Описанный в статье метод фильтрации контекста как раз к ним относится.

Этот метод — ни в коем случае не панацея. Он не гарантирует отсутствие галлюцинаций и имеет свои издержки как на разработку, так и при использовании.

Однако он довольно небольшой ценой позволяет существенно снизить число галлюцинаций, гибок в настройке, прекрасно комбинируется с другими элементами поискового пайплайна и может применяться не только в RAG, но и при фильтрации результатов в обычном поиске.

А какие методы борьбы с галлюцинациями используете вы? Пишите в комментариях.

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18 мар. 2025 г.

30.06.26 15:08 wendytaylor015

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04.07.26 15:32 Fraddy Pual

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05.07.26 14:44 lydiassmith567

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06.07.26 16:31 Joseph Weigl

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07.07.26 18:01 robertalfred175

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17.07.26 20:12 martinsjude080

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18.07.26 17:12 Malthe Larsen

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18.07.26 23:51 bernalzenaida

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19.07.26 04:10 Fraddy Pual

I can't thank Fundsretriever enough for everything they did for me. My name is Vanessa Conway, and I'm here to tell you my story of how I recovered money I never thought I'd see again. A few months ago, I put a significant amount of money into what looked like a genuine online investment company. At first, it felt real—they showed me fake profits and convinced me to invest even more. But when I tried to cash out, they went quiet and started asking for extra fees. That's when it hit me—I had been scammed. I was heartbroken, frustrated, and didn't know where to turn. That money was my savings—months of hard work gone. Then I found Fundsretriever online. I reached out, hoping for a miracle. Right away, their team made me feel heard. They were responsive, knowledgeable, and walked me through everything. They didn't just take my case—they took it seriously and kept me in the loop every step of the way. I finally felt like I had real experts fighting for me. And guess what? They actually got my money back. It wasn't instant, and it took teamwork, but it happened. When I saw those funds returned, I cried with joy. I'm sharing this so that anyone else out there who's been scammed knows—don't lose hope. Do your research before investing, and stay far away from platforms that promise too much too fast. And if you've already been stung, don't wait—get professional help immediately. Thank you, Fundsretriever, from the bottom of my heart. You didn't just recover my money—you restored my faith. — Vanessa Conway 📧 [email protected] 📱 WhatsApp: +16035121448 💬 Telegram: @Fundsretriever
19.07.26 19:53 kimberlyhebertt6877

I invested in bitcoin trading After losing $78.4 USDT) linked to a romance fraud scam worth of cryptocurrency through an online investment platform and later discovered it was a scam. After extensive research for recovery options, I contacted CAPITAL CRYPTO RECOVER based on positive client reviews and recommendations. Their professional security team guided me through the recovery process using advanced technology, and I was able to recover my lost cryptocurrency successfully. I am truly grateful for their support and assistance during such a difficult experience. I will advise you to contact CAPITAL CRYPTO RECOVER helped me recover my funds. For anyone facing similar issues, Website: https://recovercapital.wixsite.com/capital-crypto-rec-1 Email: [email protected] Telegram: @Capitalcryptorecover Contact: [email protected] Call/Text Number: +1 (336) 390-6684
19.07.26 19:53 kimberlyhebertt6877

I invested in bitcoin trading After losing $78.4 USDT) linked to a romance fraud scam worth of cryptocurrency through an online investment platform and later discovered it was a scam. After extensive research for recovery options, I contacted CAPITAL CRYPTO RECOVER based on positive client reviews and recommendations. Their professional security team guided me through the recovery process using advanced technology, and I was able to recover my lost cryptocurrency successfully. I am truly grateful for their support and assistance during such a difficult experience. I will advise you to contact CAPITAL CRYPTO RECOVER helped me recover my funds. For anyone facing similar issues, Website: https://recovercapital.wixsite.com/capital-crypto-rec-1 Email: [email protected] Telegram: @Capitalcryptorecover Contact: [email protected] Call/Text Number: +1 (336) 390-6684
19.07.26 19:54 kimberlyhebertt6877

I invested in bitcoin trading After losing $78.4 USDT) linked to a romance fraud scam worth of cryptocurrency through an online investment platform and later discovered it was a scam. After extensive research for recovery options, I contacted CAPITAL CRYPTO RECOVER based on positive client reviews and recommendations. Their professional security team guided me through the recovery process using advanced technology, and I was able to recover my lost cryptocurrency successfully. I am truly grateful for their support and assistance during such a difficult experience. I will advise you to contact CAPITAL CRYPTO RECOVER helped me recover my funds. For anyone facing similar issues, Website: https://recovercapital.wixsite.com/capital-crypto-rec-1 Email: [email protected] Telegram: @Capitalcryptorecover Contact: [email protected] Call/Text Number: +1 (336) 390-6684
30.07.26 00:04 Ahmed

A really cool analysis, thank you. I was especially struck by how strictly the order is defined: data processing and formatting come first, with color only at the very end. This really saves you from the typical mistake of "first a pretty palette, then we figure out what the chart is for." And the palette validator with OKLCH + colorblindness check is absolutely fantastic; you almost never see that in regular tools. By the way, while reading about rank-trajectory and the logarithmic scale, I immediately remembered how convenient it is to analyze dynamics on charts in ExpertOption—I've been trading there for quite some time now. When the data is well visualized, decisions are noticeably easier and more relaxed. I also liked the point about "no more than eight colors" and the dual-axis ban. Strict restrictions sometimes actually produce better results than complete freedom. I'll try this approach myself.
30.07.26 17:27 wendytaylor015

My name is Wendy Taylor, I'm from Los Angeles, i want to announce to you Viewer how Capital Crypto Recover help me to restore my Lost Bitcoin, I invested with a Crypto broker without proper research to know what I was hoarding my hard-earned money into scammers, i lost access to my crypto wallet or had your funds stolen? Don’t worry Capital Crypto Recover is here to help you recover your cryptocurrency with cutting-edge technical expertise, With years of experience in the crypto world, Capital Crypto Recover employs the best latest tools and ethical hacking techniques to help you recover lost assets, unlock hacked accounts, Whether it’s a forgotten password, Capital Crypto Recover has the expertise to help you get your crypto back. a security company service that has a 100% success rate in the recovery of crypto assets, i lost wallet and hacked accounts. I provided them the information they requested and they began their investigation. To my surprise, Capital Crypto Recover was able to trace and recover my crypto assets successfully within 24hours. Thank you for your service in helping me recover my $647,734 worth of crypto funds and I highly recommend their recovery services, they are reliable and a trusted company to any individuals looking to recover lost money. Contact email [email protected] OR Telegram @Capitalcryptorecover Call/Text Number +1 (336)390-6684 his contact: [email protected] His website: https://recovercapital.wixsite.com/capital-crypto-rec-1
30.07.26 17:27 wendytaylor015

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31.07.26 16:21 rssllhrnsb

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01.08.26 15:05 keithwilson9899

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01.08.26 15:05 keithwilson9899

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03.08.26 20:05 Philip

I was a victim of a crypto theft involving a Pink Drainer, which resulted in the theft of my Wrapped Bitcoin (WBTC) from my Polygon network. The experience was incredibly frustrating and distressing, as I had no idea how to recover my funds. Unfortunately, by the time I noticed the theft, the funds had already been drained to an address that I had no control over, making it seem like an irreversible situation.The attack happened when I clicked on what appeared to be a legitimate link. I didn’t realize at the time that it was a phishing attempt designed to siphon off my private keys and access my wallet. The Pink Drainer, a type of malicious script used by attackers, is specifically designed to exploit such vulnerabilities in crypto wallets. The moment I realized that my WBTC had been drained from my Polygon network. I felt completely helpless, as I didn’t have direct access to the thief's address, and there was no way to reverse the transaction on my own at that point, I started searching for ways to recover my funds, but most resources only offered generic advice that wasn’t practical in this particular case. I quickly realized that if I wanted to have any hope of getting my assets back, I would need professional assistance. After some research, I came across a reliable and trusted recovery team called Aspen Recovery Experts. Their expertise in cryptocurrency recovery, especially in cases like mine, seemed promising.I decided to reach out to Aspen Recovery Experts, and I’m incredibly grateful that I did. Their team of crypto recovery experts was able to help me trace the stolen funds and identify the path the funds took after they left my wallet. Using advanced tools and techniques, they were able to track the transactions on the blockchain, helping me understand where my WBTC had been sent. More importantly, they worked tirelessly to assist me in contacting the necessary parties and even interfaced with blockchain analysts to help facilitate the recovery process.Thanks to their efforts, I was able to successfully recover my stolen funds. The entire process took some time, but the Aspen Recovery Experts dedicated team provided regular updates and kept me informed throughout the process, which gave me a sense of hope and relief during an otherwise stressful time. If you’re ever in a similar situation, I highly recommend reaching out to a trusted recovery team like Aspen Recovery Expertshhh . Their professionalism, knowledge, and expertise were critical in helping me recover my funds and regain control of my crypto assets. Whatsapp : +1 747 231 9036 Telegram : @ prohackerspy Email : [email protected]
03.08.26 20:06 Philip

I was a victim of a crypto theft involving a Pink Drainer, which resulted in the theft of my Wrapped Bitcoin (WBTC) from my Polygon network. The experience was incredibly frustrating and distressing, as I had no idea how to recover my funds. Unfortunately, by the time I noticed the theft, the funds had already been drained to an address that I had no control over, making it seem like an irreversible situation.The attack happened when I clicked on what appeared to be a legitimate link. I didn’t realize at the time that it was a phishing attempt designed to siphon off my private keys and access my wallet. The Pink Drainer, a type of malicious script used by attackers, is specifically designed to exploit such vulnerabilities in crypto wallets. The moment I realized that my WBTC had been drained from my Polygon network. I felt completely helpless, as I didn’t have direct access to the thief's address, and there was no way to reverse the transaction on my own at that point, I started searching for ways to recover my funds, but most resources only offered generic advice that wasn’t practical in this particular case. I quickly realized that if I wanted to have any hope of getting my assets back, I would need professional assistance. After some research, I came across a reliable and trusted recovery team called Aspen Recovery Experts. Their expertise in cryptocurrency recovery, especially in cases like mine, seemed promising.I decided to reach out to Aspen Recovery Experts, and I’m incredibly grateful that I did. Their team of crypto recovery experts was able to help me trace the stolen funds and identify the path the funds took after they left my wallet. Using advanced tools and techniques, they were able to track the transactions on the blockchain, helping me understand where my WBTC had been sent. More importantly, they worked tirelessly to assist me in contacting the necessary parties and even interfaced with blockchain analysts to help facilitate the recovery process.Thanks to their efforts, I was able to successfully recover my stolen funds. The entire process took some time, but the Aspen Recovery Experts dedicated team provided regular updates and kept me informed throughout the process, which gave me a sense of hope and relief during an otherwise stressful time. If you’re ever in a similar situation, I highly recommend reaching out to a trusted recovery team like Aspen Recovery Expertshhh . Their professionalism, knowledge, and expertise were critical in helping me recover my funds and regain control of my crypto assets. Whatsapp : +1 747 231 9036 Telegram : @ prohackerspy Email : [email protected]
04.08.26 11:05 Kisnoles

Excellent analysis, thank you. I was particularly struck by how rigidly the skill sets the order: data processing and form come first, with color coming last. This really cures the habit of "first a pretty palette, then we'll figure it out." A palette validator using OKLCH + color blindness + WCAG is something most chart generators lack. And regarding the boundaries of competence, it's very clear. Where there's a self-checking loop (color), you delegate freely. Where there's a heuristic (form, data interpretation), you remain the final filter. This is a universal principle, not just for /dataviz. By the way, when you look at trading dashboards (including those of decent platforms like ExpertOption), it's immediately clear who thought about readability and who just threw in rainbow lines. Tools like this skill could greatly improve the quality of analytics. Thanks again for the detailed analysis – saved.
04.08.26 12:37 kimberlyhebertt6877

I invested in bitcoin trading After losing $78.4 USDT) linked to a romance fraud scam worth of cryptocurrency through an online investment platform and later discovered it was a scam. After extensive research for recovery options, I contacted CAPITAL CRYPTO RECOVER based on positive client reviews and recommendations. Their professional security team guided me through the recovery process using advanced technology, and I was able to recover my lost cryptocurrency successfully. I am truly grateful for their support and assistance during such a difficult experience. I will advise you to contact CAPITAL CRYPTO RECOVER helped me recover my funds. For anyone facing similar issues, Website: https://recovercapital.wixsite.com/capital-crypto-rec-1 Email: [email protected] Telegram: @Capitalcryptorecover Contact: [email protected] Call/Text Number: +1 (336) 390-6684
04.08.26 12:37 kimberlyhebertt6877

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06.08.26 08:11 ROMMYHENDERSON344

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06.08.26 13:56 lydiassmith567

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11.08.26 03:43 raymont0714

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12.08.26 16:37 rssllhrnsb

I learned an important lesson after investing in what appeared to be a genuine opportunity. Unfortunately, I was unable to access my funds, which reinforced the importance of carrying out thorough due diligence, checking whether an investment is appropriately regulated, and seeking qualified professional advice rather than relying solely on online reviews or testimonials. During the process of addressing my case, I worked with Mrs. Tatiana Sorina , who communicated clearly, provided regular updates, and handled the matter in a professional manner. According to my experience, I have recovered $50,000 so far, while efforts to resolve the remaining balance are still in progress. If you wish to contact her, the details I used are: Mrs. Tatiana Sorina TEXT : (tatianasorina06 at G_Ma IL dot ..c 0 m)…. Before committing money to any investment, take time to verify the legitimacy of the platform, confirm any relevant regulatory authorisations, and avoid investing more than you can comfortably afford to lose.
16.08.26 01:44 Matt Kegan

CapitalNode Analytics. They help to investigate and recover stolen digital assets from fake trading platforms. Great firm i must say.
18.08.26 04:59 marcushenderson624

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18.08.26 04:59 marcushenderson624

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19.08.26 14:50 BAYER7043

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20.08.26 11:32 michaeldavenport218

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20.08.26 11:32 michaeldavenport218

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23.08.26 20:02 leslieyee

Excellent analysis by /dataviz! I was particularly struck by how strictly the order is defined: "data meaning first, color last" and how the validator actually adjusts the palette according to OKLCH, color blindness, and WCAG standards. These rules greatly improve the quality of charts. By the way, a similar approach to clean and legible charts is highly valued in trading—for example, on the ExpertOption platform, the interface and price visualization are designed to ensure information is read instantly and without unnecessary noise.
25.08.26 13:44 lydiassmith567

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25.08.26 13:44 lydiassmith567

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30.08.26 15:37 [email protected]

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30.08.26 15:37 [email protected]

Discovering I had been defrauded, I searched online for services claiming to recover stolen ETH. I contacted several companies, but none succeeded. Although some claimed they could trace assets or recover funds, I could not verify their success or recover my money. I later found SYLVESTER BRYANT Recovery through Google. After contacting him, he recovered 450,000,00. He can be reached at [email protected] or WhatsApp at +1 512 577 7957.
01.09.26 11:16 lisawerth897

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01.09.26 11:16 lisawerth897

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01.09.26 17:34 Garry42

Really crazy world. These fraudsters go at any length to steal your hard earned funds. I have been a victim of a bitcoin scam about 7 months back. A Con artist gained access to my cashapp account through a phishing scam. They stole $409,000. I was really devastated. I did everything to get back my funds by contacting the FBI but they claimed there was nothing they could do. A friend told me about a recovery expert. He helps fight against various phishing and investment scams and they were able to help trace and recover my funds even though it took over 2 days. you can reach out to him through recoverydarek@gmail. com . I can guarantee his services are still active.
01.09.26 17:34 Garry42

Really crazy world. These fraudsters go at any length to steal your hard earned funds. I have been a victim of a bitcoin scam about 7 months back. A Con artist gained access to my cashapp account through a phishing scam. They stole $409,000. I was really devastated. I did everything to get back my funds by contacting the FBI but they claimed there was nothing they could do. A friend told me about a recovery expert. He helps fight against various phishing and investment scams and they were able to help trace and recover my funds even though it took over 2 days. you can reach out to him through recoverydarek@gmail. com . I can guarantee his services are still active.
02.09.26 03:43 kimberlyhebertt6877

I invested in bitcoin trading After losing $78.4 USDT) linked to a romance fraud scam worth of cryptocurrency through an online investment platform and later discovered it was a scam. After extensive research for recovery options, I contacted CAPITAL CRYPTO RECOVER based on positive client reviews and recommendations. Their professional security team guided me through the recovery process using advanced technology, and I was able to recover my lost cryptocurrency successfully. I am truly grateful for their support and assistance during such a difficult experience. I will advise you to contact CAPITAL CRYPTO RECOVER helped me recover my funds. For anyone facing similar issues, Website: https://recovercapital.wixsite.com/capital-crypto-rec-1 Email: [email protected] Telegram: @Capitalcryptorecover Contact: [email protected] WhatsApp/Text Number: +1 (336) 390-6684
02.09.26 03:43 kimberlyhebertt6877

I invested in bitcoin trading After losing $78.4 USDT) linked to a romance fraud scam worth of cryptocurrency through an online investment platform and later discovered it was a scam. After extensive research for recovery options, I contacted CAPITAL CRYPTO RECOVER based on positive client reviews and recommendations. Their professional security team guided me through the recovery process using advanced technology, and I was able to recover my lost cryptocurrency successfully. I am truly grateful for their support and assistance during such a difficult experience. I will advise you to contact CAPITAL CRYPTO RECOVER helped me recover my funds. For anyone facing similar issues, Website: https://recovercapital.wixsite.com/capital-crypto-rec-1 Email: [email protected] Telegram: @Capitalcryptorecover Contact: [email protected] WhatsApp/Text Number: +1 (336) 390-6684
04.09.26 21:51 Kovengray

Recovering your lost investment funds as the case might be, is not what you can do alone, you’d require the service of a trained recovery specialist. A recovery specialist is a person or a group of people who are well equipped to work around the brokerage network. They have vast knowledge about the whole network and have the right software and private keys to follow any transaction. I was ripped off trading online to an investment broker, good thing I got every penny back through the help of Gavin ray he’s a genius Contact : Gavinray78 at gmail com or WhatsApp +1 352 322 2096 It is also important to be patient and really calm during the process.
05.09.26 21:38 [email protected]

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09.09.26 21:31 lisawerth897

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15.09.26 03:35 elioduncan

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05.10.26 21:26 [email protected]

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06.10.26 06:13 melissathomason573

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06.10.26 06:13 melissathomason573

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06.10.26 17:44 Darrell1717

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11.10.26 03:04 Patricia014

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13.10.26 14:14 lisalisa22

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13.10.26 14:14 lisalisa22

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17.10.26 12:58 melissathomason573

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17.10.26 12:58 melissathomason573

Professional Crypto Recovery Testimonial After losing a significant amount of cryptocurrency through a scam on Twitter, I was unsure whether my Bitcoin funds could ever be recovered. The experience was extremely stressful, particularly because I encountered several individuals and services claiming they could help recover my assets, only to discover that some were fraudulent themselves. A friend eventually referred me to CAPITAL CRYPTO RECOVER. Their team handled my case professionally, discreetly, and efficiently. With their assistance, I successfully recovered approximately $130,000 worth of USDT in just over a week. My experience taught me the importance of being extremely cautious when seeking cryptocurrency recovery services, as scammers often target victims a second time. I am grateful to CAPITAL CRYPTO RECOVER for the assistance I received and wanted to share my experience with others who may be facing a similar situation. If you’ve been compromised, don’t lose hope—and be careful of fraudsters posing as saviors. CAPITAL CRYPTO RECOVER are real professionals in crypto recovery. I am a living proof of their effectiveness, you can reach them through Phone (Call/WhatsApp): +1 (336) 390-6684 Email: [email protected] Alternate Email: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1
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25.10.26 14:35 lydiasmitth456

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25.10.26 14:35 lydiasmitth456

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27.10.26 01:58 KATHLEEN

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27.10.26 02:22 kimberlyhebertt6877

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27.10.26 02:22 kimberlyhebertt6877

I invested in bitcoin trading After losing $78.4 USDT) linked to a romance fraud scam worth of cryptocurrency through an online investment platform and later discovered it was a scam. After extensive research for recovery options, I contacted CAPITAL CRYPTO RECOVER based on positive client reviews and recommendations. Their professional security team guided me through the recovery process using advanced technology, and I was able to recover my lost cryptocurrency successfully. I am truly grateful for their support and assistance during such a difficult experience. I will advise you to contact CAPITAL CRYPTO RECOVER helped me recover my funds. For anyone facing similar issues, Website: https://recovercapital.wixsite.com/capital-crypto-rec-1 Email: [email protected] Telegram: @Capitalcryptorecover Contact: [email protected] WhatsApp/Text Number: +1 (336) 390-6684
27.10.26 02:25 KATHLEEN

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