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[Перевод] Метрики оценки LLM: полное руководство по оценке LLM

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Независимо от того, улучшаете ли вы точность модели путем дообучения или улучшаете контекстную релевантность системы генерации с дополненной выборкой (RAG), понимание того, как разрабатывать и выбирать подходящий набор метрик оценки LLM для вашего варианта использования, является обязательным для построения надежного конвейера оценки LLM.

В этой статье вы научитесь всему, что нужно знать о метриках оценки LLM, включая примеры кода. Мы рассмотрим следующие вопросы:

  • Что такое метрики оценки LLM, как их можно использовать для оценки систем LLM, а также распространенные ошибки и что делает метрики отличными.

  • Различные методы вычисления метрик оценки LLM и почему подход LLM-as-a-judge («LLM как судья») является наиболее эффективным.

  • Как реализовать и выбрать подходящий набор метрик оценки LLM с использованием библиотеки DeepEval (GitHub: DeepEval).

Что такое метрики оценки LLM?

Метрики оценки LLM, такие как правильность ответа, семантическое сходство и галлюцинации, — это метрики, которые оценивают выходные данные системы LLM на основе критериев, которые вас интересуют. Они имеют решающее значение для оценки LLM, так как помогают количественно измерить производительность различных систем на основе LLM, включая саму модель.

Архитектура метрик оценки LLM
Архитектура метрик оценки LLM

Вот наиболее важные и общие метрики, которые вам, скорее всего, понадобятся перед запуском вашей системы LLM в производство:

  1. Релевантность ответа: определяет, могут ли выходные данные LLM отвечать заданным входным данным информативно и кратко.

  2. Соответствие запросу: оценивает, следует ли вывод LLM инструкциям из шаблона запроса.

  3. Точность: определяет, является ли вывод LLM фактически корректным на основе проверенной истины (ground truth).

  4. Галлюцинации: определяет, содержат ли выходные данные LLM ложную или выдуманную информацию.

  5. Контекстная релевантность: определяет, способен ли механизм извлечения информации в RAG-архитектуре выделять наиболее релевантные данные для контекста, используемого LLM.

  6. Метрики ответственности: такие метрики, как предвзятость и токсичность, определяющие, содержат ли выходные данные LLM вредный и оскорбительный контент.

  7. Задачно-специфические метрики: включают показатели, такие как оценка качества резюмирования текста, которые обычно разрабатываются с учетом конкретных случаев использования.

Большинство метрик являются общими и обязательными, однако их недостаточно для определения конкретных вариантов использования. Вот почему вам понадобится по крайней мере одна пользовательская метрика, специфичная для конкретной задачи, чтобы подготовить ваш конвейер оценки LLM к производству. Например, если ваше приложение LLM предназначено для резюмирования страниц новостных статей, вам понадобится пользовательская метрика оценки LLM, оценивающая:

  1. Содержит ли резюме достаточно информации из исходного текста.

  2. Содержит ли резюме какие-либо противоречия или галлюцинации из исходного текста.

Более того, если ваше приложение LLM имеет архитектуру на основе RAG, вам, вероятно, также нужно будет оценить качество контекста извлечения. Суть в том, что метрика оценки LLM оценивает приложение LLM на основе задач, для которых оно было разработано. (Обратите внимание, что приложение LLM может представлять собой просто саму модель LLM!)

Отличными метриками оценки являются:

  1. Количественность. Метрики всегда должны вычислять балл при оценке поставленной задачи. Этот подход позволяет вам установить минимальный порог прохода, чтобы определить, является ли ваше приложение LLM «достаточно хорошим», и позволяет вам отслеживать, как эти баллы меняются со временем по мере улучшения моделм.

  2. Надежность. Какими бы непредсказуемыми ни были результаты LLM, последнее, чего вы хотите, — это чтобы метрика оценки LLM оказалась ненадежной. Хотя метрики, основанные на оценке с помощью LLM (LLM-as-a-judge или LLM-Evals), такие как G-Eval, обычно точнее традиционных методов, они нередко бывают непоследовательными, что является их основной слабостью.

  3. Точность. Надежные результаты бессмысленны, если они не отражают реальную производительность вашего приложения. Секрет успешной метрики оценки заключается в том, чтобы максимально согласовать ее с ожиданиями человека.

Таким образом, возникает вопрос, как метрики оценки LLM могут обеспечивать одновременно надежные и точные показатели?

Различные способы вычисления метрических баллов

В одной из предыдущих статей я рассказывал о том, как сложно оценивать выходные данные LLM. К счастью, существует множество проверенных методов для вычисления оценочных метрик. Некоторые из них используют нейронные сети, включая модели эмбеддингов и сами LLM, тогда как другие основаны исключительно на статистическом анализе.

Типы метрических оценщиков
Типы метрических оценщиков

В конце этого раздела мы рассмотрим каждый метод и поговорим о наилучшем подходе.

Статистические оценщики

Я хочу начать с того, что статистические методы оценки, по моему мнению, не являются обязательными для изучения. Это связано с тем, что статистические методы плохо справляются с задачами, требующими рассуждений, и оказываются слишком неточными для большинства критериев оценки LLM.

Вкратце:

  • BLEU (BiLingual Evaluation Understudy) — оценивает выводы вашей LLM по сравнению с аннотированными эталонами (или ожидаемыми результатами). BLEU вычисляет точность для каждого совпадающего n-грамма (n последовательных слов) между выводом LLM и эталоном, чтобы найти их геометрическое среднее, и применяет штраф за краткость, если это необходимо.

  • ROUGE (Recall-Oriented Understudy for Gisting Evaluation) — чаще всего используется для оценки текстовых резюме, генерируемых NLP-моделями. ROUGE вычисляет полноту, сравнивая перекрытие n-граммов между выводами LLM и эталонами, определяя долю (от 0 до 1) n-граммов эталона, которые присутствуют в выводах LLM.

  • METEOR (Metric for Evaluation of Translation with Explicit Ordering) — более комплексный метод, который оценивает как точность (совпадения n-граммов), так и полноту (перекрытие n-граммов), с учетом порядка слов в выводах LLM и эталонах. METEOR также использует внешние лингвистические базы данных, такие как WordNet, для учета синонимов. Итоговый балл вычисляется как гармоническое среднее точности и полноты с учетом штрафа за нарушения порядка.

  • Расстояние Левенштейна — вычисляет минимальное количество правок одного символа (вставки, удаления или замены), необходимых для преобразования одного слова или строки текста в другую. Этот метод полезен для оценки исправлений орфографических ошибок или задач, где важна точная выравненность символов.

Поскольку чисто статистические оценщики почти не учитывают семантику и имеют крайне ограниченные возможности рассуждения, они недостаточно точны для оценки выходных данных LLM, которые часто бывают длинными и сложными.

Оценщики на основе моделей

Статистические методы оценки надежны, но недостаточно точны, так как не учитывают семантику. В этой категории всё наоборот — методы, полностью зависящие от NLP-моделей, более точны, но менее надежны из-за своей вероятностной природы.

Это неудивительно, но методы оценки, не основанные на LLM, показывают худшие результаты по сравнению с LLM-as-a-judge, по тем же причинам, что и статистические методы. Среди них:

  • NLI (Natural Language Inference), который использует модели вывода на естественном языке (что является типом модели классификации NLP) для классификации того, являются ли выходные данные LLM логически последовательными (вывод), противоречивыми или несвязанными (нейтральными) по отношению к заданному справочному тексту. Оценка обычно варьируется между выводом (со значением 1) и противоречием (со значением 0), обеспечивая меру логической связности.

  • BLEURT (Bilingual Evaluation Understudy with Representations from Transformers) использует предварительно обученные модели, такие как BERT, для оценки выводов LLM на основе ожидаемых результатов.

Помимо несогласованности оценок, такие подходы имеют ряд других недостатков. Например, NLI может испытывать трудности с точностью при обработке длинных текстов, а BLEURT ограничен качеством и репрезентативностью данных, на которых он был обучен.

Теперь перейдем к LLM-судьям.

G-Eval

G-Eval — это недавно разработанная структура из статьи под названием «Оценка NLG с использованием GPT-4 с улучшенным человеческим соответствием», которая использует LLM для оценки выходных данных других LLM, и является одним из лучших способов создания метрик, специфичных для задач.

Алгоритм G-Eval
Алгоритм G-Eval

Как было описано в одной из моих предыдущих статей, G-Eval сначала генерирует последовательность шагов оценки с использованием метода цепочки рассуждений (Chain of Thoughts, CoTs), а затем применяет сгенерированные шаги для определения итоговой оценки через парадигму заполнения формы (проще говоря, G-Eval требует нескольких компонентов информации для выполнения своей работы). Например, для оценки связности вывода LLM с помощью G-Eval необходимо составить запрос, включающий критерии и текст для оценки, чтобы сначала сгенерировать шаги оценки, а затем с помощью LLM вывести итоговый балл по шкале от 1 до 5, опираясь на эти шаги.

Рассмотрим алгоритм G-Eval на следующем примере. Чтобы сгенерировать шаги оценки:

  1. Представьте задачу оценки выбранной LLM (например, «оцените этот вывод по шкале от 1 до 5, основываясь на связности»).

  2. Определите критерий оценки (например, «Связность — это совокупное качество всех предложений в данном выводе»).

    (Обратите внимание, что в оригинальной статье о G-Eval авторы использовали только GPT-3.5 и GPT-4 для экспериментов. Основываясь на личном опыте с различными моделями, я настоятельно рекомендую придерживаться именно этих моделей.)

    После генерации последовательности шагов оценки:

  3. Составьте запрос, объединив шаги оценки с аргументами, перечисленными в этих шагах (например, если вы оцениваете связность вывода LLM, то сам вывод модели будет обязательным аргументом).

  4. В конце запроса попросите модель вывести итоговую оценку от 1 до 5, где 5 означает лучшее качество.

  5. (Опционально) Используйте вероятности выходных токенов из LLM для нормализации итоговой оценки и вычисления взвешенной суммы в качестве конечного результата.

Шаг 5 является необязательным, поскольку для получения вероятностей выходных токенов необходим доступ к эмбеддингам модели, который не всегда доступен во всех интерфейсах LLM. Однако этот шаг был введен в статье, так как он позволяет получить более точные оценки и минимизировать предвзятость при оценке с помощью LLM (в статье отмечено, что токен "3" имеет более высокую вероятность на шкале от 1 до 5).

Вот результаты из статьи, показывающие, как G-Eval превосходит все традиционные оценки, не основанные на LLM, упомянутые ранее:

Более высокая корреляция Спирмена и Кендалла-Тау представляет более высокую согласованность с человеческим суждением.
Более высокая корреляция Спирмена и Кендалла-Тау представляет более высокую согласованность с человеческим суждением.

G-Eval хорош тем, что являясь LLM-Eval, он способен учитывать полную семантику выводов, генерируемых LLM, что делает его значительно более точным. И это вполне логично — подумайте сами: как методы оценки, не основанные на LLM, которые используют значительно менее мощные инструменты, чем сами LLM, могут в полной мере понимать весь объем текста, созданного языковыми моделями?

Хотя G-Eval гораздо больше коррелирует с человеческим суждением по сравнению со своими аналогами, он все равно может быть ненадежным, поскольку просьба к LLM выдать оценку по своей сути остается произвольной.

При этом, учитывая, насколько гибкими могут быть критерии оценки G-Eval, я реализовал G-Eval в качестве метрики для DeepEval, фреймворка оценки LLM с открытым исходным кодом, над которым я работал (он включает в себя метод нормализации из оригинальной статьи).

# Install
pip install deepeval
# Set OpenAI API key as env variable
export OPENAI_API_KEY="..."
from deepeval.test_case import LLMTestCase, LLMTestCaseParams
from deepeval.metrics import GEval

test_case = LLMTestCase(input="input to your LLM", actual_output="your LLM output")
coherence_metric = GEval(
    name="Coherence",
    criteria="Coherence - the collective quality of all sentences in the actual output",
    evaluation_params=[LLMTestCaseParams.ACTUAL_OUTPUT],
)

coherence_metric.measure(test_case)
print(coherence_metric.score)
print(coherence_metric.reason)

Еще одно важное преимущество использования LLM-Eval заключается в том, что LLM способны объяснить причины, по которым была присвоена та или иная оценка.

Prometheus

Prometheus — это полностью открытый исходный код LLM, который сопоставим с возможностями оценки GPT-4, при условии предоставления соответствующих справочных материалов (эталонный ответ, шкала оценки). Как и G-Eval, Prometheus является независимой от конкретных задач системой. Эта языковая модель построена на базе Llama-2-Chat и дообучена на основе 100 тысяч отзывов, сгенерированных GPT-4 в рамках сбора обратной связи.

Вот краткие результаты исследовательской работы Prometheus.

Причина, по которой отзывы GPT-4 или Prometheus не были выбраны вместо других - Prometheus генерирует менее абстрактные и общие отзывы, но имеет тенденцию писать чрезмерно критические отзывы
Причина, по которой отзывы GPT-4 или Prometheus не были выбраны вместо других - Prometheus генерирует менее абстрактные и общие отзывы, но имеет тенденцию писать чрезмерно критические отзывы

Prometheus следует тем же принципам, что и G-Eval. Однако есть несколько отличий:

  • Если G-Eval является фреймворком, использующим GPT-3.5/4, то Prometheus — это языковая модель, специально дообученная для задач оценки.

  • В G-Eval критерии оценки и шаги генерируются с использованием цепочек рассуждений (Chain of Thoughts, CoTs), тогда как в Prometheus шкала оценки предоставляется непосредственно в запросе (prompt).

  • Для работы Prometheus требуются справочные/примерные результаты оценки.

Prometheus доступен на Hugging Face.

Несмотря на это, я лично пока не пробовал его внедрять. Основная цель Prometheus заключается в том, чтобы сделать процессы оценки открытыми, устраняя зависимость от проприетарных моделей, таких как GPT от OpenAI. Однако, для тех, кто стремится создать лучшие доступные методы оценки LLM, Prometheus может оказаться не самым подходящим выбором.

Объединение статистических и основанных на моделях оценщиков

К настоящему моменту мы убедились в том, что статистические методы надежны, но неточны, а подходы, не основанные на моделях LLM, менее надежны, но более точны. Подобно предыдущему разделу, существуют и другие оценщики, не использующие LLM, такие как:

  • Оценщик BERTScore, который опирается на предварительно обученные языковые модели, такие как BERT, и вычисляет косинусное сходство между контекстуальными выстраиваниями слов в эталонном тексте и сгенерированном тексте. Эти сходства затем агрегируются для получения итогового балла. Чем выше BERTScore, тем больше семантическое совпадение между выводом LLM и эталонным текстом.

  • Оценщик MoverScore, который использует модели встраивания, в частности, предварительно обученные языковые модели, такие как BERT, для получения глубоко контекстуализированных встраиваний слов как для эталонного текста, так и для сгенерированного текста. Затем используется метрика, называемая Earth Mover’s Distance (EMD) для вычисления минимальной стоимости, которую необходимо заплатить, чтобы преобразовать распределение слов в выводе LLM в распределение слов в эталонном тексте.

Обе метрики — BERTScore и MoverScore — уязвимы к контекстуальной осведомленности и предвзятости из-за их зависимости от контекстуальных встраиваний, полученных из предварительно обученных моделей, таких как BERT. Но что насчет LLM-оценок?

GPTScore

В отличие от G-Eval, который напрямую выполняет задачу оценки с парадигмой заполнения форм, GPTScore использует условную вероятность генерации целевого текста в качестве метрики оценки.

Алгоритм GPTScore
Алгоритм GPTScore

SelfCheckGPT

SelfCheckGPT — необычный подход. Он представляет собой простой метод, основанный на выборке, который используется для проверки фактов в выводах LLM. Он предполагает, что галлюцинированные (выдуманные) выводы не воспроизводимы, в то время как если LLM знает какой-либо концепт, то выборочные ответы, вероятно, будут схожи и содержать последовательные факты.

SelfCheckGPT — интересный подход, поскольку он делает обнаружение галлюцинаций процессом без ссылок, что чрезвычайно полезно в производственных условиях.

Алгоритм SelfCheckGPT
Алгоритм SelfCheckGPT

Однако, хотя G-Eval и Prometheus являются независимыми от конкретной задачи, SelfCheckGPT не так универсален. Он подходит только для обнаружения галлюцинаций и не может быть использован для оценки других задач, таких как суммаризация, когерентность и другие.

QAG Score

QAG (Question Answer Generation) Score — это оценщик, который использует высокие возможноссти рассуждений LLM для надежной оценки выходных данных LLM. Он использует ответы (обычно «да» или «нет») на закрытые вопросы (которые могут быть сгенерированы или заранее заданы), чтобы вычислить итоговый балл метрики. Он надежен, потому что не использует LLM для прямой генерации оценок. Например, если вы хотите вычислить оценку за точность (что измеряет, был ли вывод LLM галлюцинированным или нет), вы бы:

  • Использовали LLM для извлечения всех утверждений, сделанных в выводе LLM.

  • Для каждого утверждения спрашивали бы у эталонной истины, согласна ли она с этим утверждением («да») или нет («нет»).

Давайте разберем следующий пример выходных данных LLM:

Мартин Лютер Кинг-младший, известный лидер движения за гражданские права, был убит 4 апреля 1968 года в мотеле Lorraine в Мемфисе, штат Теннесси. Он был в Мемфисе, чтобы поддержать бастующих работников санитарной службы, и был смертельно ранен сбежавшим заключенным Джеймсом Эрлом Рэем, стоя на балконе второго этажа мотеля.

Утверждение будет выглядеть следующим образом:

Мартин Лютер Кинг-младший был убит 4 апреля 1968 года

А соответствующий закрытый вопрос будет следующим:

Был ли Мартин Лютер Кинг-младший убит 4 апреля 1968 года?

Затем вы берете этот вопрос и спрашиваете, согласна ли эталонная истина с утверждением. В результате у вас будет набор ответов «да» и «нет», которые можно использовать для вычисления оценки с помощью любой выбранной математической формулы.

В случае метрики достоверности, если мы определим её как долю утверждений в выводе LLM, которые точны и согласованы с эталонной истиной, её можно легко рассчитать, разделив количество точных (правдивых) утверждений на общее количество утверждений, сделанных моделью LLM. Поскольку в этом подходе мы не используем LLM для прямого генерации оценок, но при этом задействуем её превосходные способности к рассуждению, мы получаем оценки, которые одновременно являются точными и надёжными.

Выбор метрик оценки

Выбор метрик оценки LLM зависит от варианта использования и архитектуры вашего приложения LLM.

Например, если вы создаете чат-бота поддержки клиентов на основе RAG, а также на основе моделей GPT OpenAI, вам нужно будет использовать несколько метрик RAG (например, верность, релевантность ответа, контекстная точность), а если вы настраиваете свой собственный Mistral 7B, вам понадобятся такие метрики, как предвзятость, чтобы гарантировать беспристрастные решения LLM.

В этом последнем разделе мы рассмотрим метрики оценки, которые вам обязательно нужно знать (и в качестве бонуса — реализацию каждой из них).

Метрики RAG

Если вкратце, RAG добавляет в LLM дополнительный контекст для генерации адаптированных выходных данных, и он отлично подходит для создания чат-ботов. Он состоит из двух компонентов — извлекателя и генератора.

Типичная архитектура RAG
Типичная архитектура RAG

Вот как обычно выглядит рабочий процесс RAG:

  1. Ваша система RAG получает входные данные.

  2. Извлекатель использует эти входные данные для выполнения векторного поиска в вашей базе знаний (которая в настоящее время в большинстве случаев является векторной базой данных).

  3. Генератор получает контекст извлечения и пользовательские входные данные в качестве дополнительного контекста для генерации адаптированных выходных данных.

Высококачественные результаты работы LLM являются результатом отличной работы как извлекателя, так и генератора. Именно поэтому эффективные метрики для оценки RAG-систем сосредоточены на надёжной и точной оценке либо извлекателя, либо генератора. (Фактически, изначально метрики RAG были разработаны как метрики, не зависящие от эталонов, что делает их пригодными даже в условиях продакшена.)

Достоверность

Достоверность — это метрика RAG, которая оценивает, генерирует ли LLM/генератор в вашем RAG-пайплайне результаты, которые фактически соответствуют информации, представленной в контексте извлечения. Но какой оценщик нам следует использовать для метрики достоверности?

Спойлер: оценщик QAG является лучшим оценщиком для метрик RAG, поскольку он отлично подходит для задач, где цель чётко определена. Для достоверности, если вы определяете её как долю правдивых утверждений в выводе LLM относительно контекста извлечения, достоверность можно рассчитать с помощью QAG, следуя этому алгоритму:

  1. Используйте LLM для извлечения всех утверждений, сделанных в выходных данных.

  2. Для каждого утверждения проверьте, согласуется ли оно или противоречит каждому отдельному узлу в контексте извлечения. В данном случае закрытый вопрос в QAG будет звучать примерно так: «Соответствует ли данное утверждение тексту из справочного источника», где «справочный источник» — это каждый отдельный извлечённый узел. (Обратите внимание, что ответ должен быть строго ограничен вариантами «да», «нет» или «неизвестно». Состояние «неизвестно» используется в случае, если контекст извлечения не содержит достаточной информации для ответа.)

  3. Сложите общее количество правдивых утверждений («да» и «неизвестно») и разделите его на общее количество сделанных утверждений.

Этот метод обеспечивает точность, используя продвинутые способности LLM к рассуждению, при этом избегая ненадёжности, связанной с оценками, сгенерированными самим LLM. Таким образом, он превосходит метод G-Eval в качестве метрики.

Если вы считаете, что это слишком сложно реализовать, вы можете использовать DeepEval. Это пакет с открытым исходным кодом, который предлагает все метрики оценки, необходимые для оценки LLM, включая метрику достоверности.

# Install
pip install deepeval
# Set OpenAI API key as env variable
export OPENAI_API_KEY="..."
from deepeval.metrics import FaithfulnessMetric
from deepeval.test_case import LLMTestCase

test_case=LLMTestCase(
  input="...", 
  actual_output="...",
  retrieval_context=["..."]
)
metric = FaithfulnessMetric(threshold=
0.5
)

metric.measure(test_case)
print(metric.score)
print(metric.reason)
print(metric.is_successful())

DeepEval рассматривает оценку как тестовые случаи. Здесь actual_output — это просто выходные данные LLM. Кроме того, поскольку достоверность — это оценка LLM, вы можете получить обоснование для окончательного рассчитанного балла.

Релевантность ответа

Релевантность ответа — это метрика RAG, которая оценивает, выдает ли ваш генератор RAG лаконичные и точные ответы, и она может быть рассчитана путем определения доли предложений в выходных данных LLM, которые релевантны входным данным (т. е. путем деления числа релевантных предложений на общее число предложений).

Ключом к построению надежной метрики релевантности ответа является учет контекста извлечения, поскольку дополнительный контекст может оправдать релевантность, казалось бы, нерелевантного предложения. Вот как можно реализовать метрику релевантности ответа:

from deepeval.metrics import AnswerRelevancyMetric
from deepeval.test_case import LLMTestCase

test_case=LLMTestCase(
  input="...", 
  actual_output="...",
  retrieval_context=["..."]
)
metric = AnswerRelevancyMetric(threshold=
0.5
)

metric.measure(test_case)
print(metric.score)
print(metric.reason)
print(metric.is_successful())

(Помните, что мы используем QAG для всех метрик RAG)

Контекстная точность

Контекстная точность — это метрика RAG, которая оценивает качество извлекателя вашего конвейера RAG. Когда мы говорим о контекстных метриках, нас в основном интересует релевантность контекста извлечения. Высокая оценка контекстной точности означает, что узлы, которые релевантны в контексте извлечения, ранжируются выше, чем нерелевантные. Это важно, поскольку LLM придает больший вес информации в узлах, которые появляются раньше в контексте извлечения, что влияет на качество конечных выходных данных.

from deepeval.metrics import ContextualPrecisionMetric
from deepeval.test_case import LLMTestCase

test_case=LLMTestCase(
  input="...", 
  actual_output="...",
  # Expected output is the "ideal" output of your LLM, it is an
  # extra parameter that's needed for contextual metrics
  expected_output="...",
  retrieval_context=["..."]
)
metric = ContextualPrecisionMetric(threshold=
0.5
)

metric.measure(test_case)
print(metric.score)
print(metric.reason)
print(metric.is_successful())

Контекстная полнота

Контекстная полнота — это дополнительная метрика для оценки генератора дополненного извлечения (RAG). Она рассчитывается путем определения доли предложений в ожидаемых выходных данных или основной истине, которую можно отнести к узлам в контексте извлечения. Более высокая оценка представляет большую согласованность между извлеченной информацией и ожидаемыми выходными данными, указывая на то, что извлекатель эффективно извлекает релевантный и точный контент, чтобы помочь генератору в создании контекстно-подходящих ответов.

from deepeval.metrics import ContextualRecallMetric
from deepeval.test_case import LLMTestCase

test_case=LLMTestCase(
  input="...", 
  actual_output="...",
  # Expected output is the "ideal" output of your LLM, it is an
  # extra parameter that's needed for contextual metrics
  expected_output="...",
  retrieval_context=["..."]
)
metric = ContextualRecallMetric(threshold=
0.5
)

metric.measure(test_case)
print(metric.score)
print(metric.reason)
print(metric.is_successful())

Контекстная релевантность

Это, вероятно, самая простая для понимания метрика. Контекстная релевантность — это доля предложений в контексте извлечения, которые релевантны заданным входным данным.

from deepeval.metrics import ContextualRelevancyMetric
from deepeval.test_case import LLMTestCase

test_case=LLMTestCase(
  input="...", 
  actual_output="...",
  retrieval_context=["..."]
)
metric = ContextualRelevancyMetric(threshold=
0.5
)

metric.measure(test_case)
print(metric.score)
print(metric.reason)
print(metric.is_successful())

Метрики для дообучения

Когда я говорю о метриках для дообучения (fine-tuning metrics), я имею в виду метрики, оценивающие саму LLM, а не всю систему. Если оставить в стороне стоимость и преимущества в производительности, LLM часто дообучают для того, чтобы:

  • Интегрировать дополнительное контекстное знание.

  • Скорректировать поведение модели.

Галлюцинации

Некоторые из вас могут заметить сходство этой метрики с метрикой достоверности. Хотя они действительно схожи, галлюцинации в контексте дообучения более сложны, так как часто сложно определить точную эталонную истину для конкретного вывода. Чтобы обойти эту проблему, можно воспользоваться подходом SelfCheckGPT, который в режиме zero-shot оценивает долю галлюцинированных предложений в выводе LLM.

from deepeval.metrics import HallucinationMetric
from deepeval.test_case import LLMTestCase

test_case=LLMTestCase(
  input="...", 
  actual_output="...",
  # Note that 'context' is not the same as 'retrieval_context'.
  # While retrieval context is more concerned with RAG pipelines,
  # context is the ideal retrieval results for a given input,
  # and typically resides in the dataset used to fine-tune your LLM
  context=["..."],
)
metric = HallucinationMetric(threshold=
0.5
)

metric.measure(test_case)
print(metric.score)
print(metric.is_successful())

Однако этот подход может оказаться очень дорогостоящим, поэтому я бы предложил использовать оценщик NLI и вручную предоставить некоторый контекст в качестве истины.

Токсичность

Метрика токсичности оценивает степень, в которой текст содержит оскорбительный, вредный или неуместный язык. Для оценки токсичности можно использовать готовые предварительно обученные модели, такие как Detoxify, которые используют оценщика BERT.

from deepeval.metrics import ToxicityMetric
from deepeval.test_case import LLMTestCase

metric = ToxicityMetric(threshold=
0.5
)
test_case = LLMTestCase(
    input="What if these shoes don't fit?",
    # Replace this with the actual output from your LLM application
    actual_output = "We offer a 30-day full refund at no extra cost."
)

metric.measure(test_case)
print(metric.score)

Однако этот метод может быть неточным, так как слова, "ассоциирующиеся с ругательствами, оскорблениями или ненормативной лексикой, могут классифицироваться как токсичные вне зависимости от тона или намерений автора (например, в случае юмористического или самоироничного комментария)".

В этом случае вы можете рассмотреть использование G-Eval для определения пользовательских критериев токсичности. На самом деле, именно универсальность G-Eval — одна из причин, по которой я предпочитаю его.

from deepeval.metrics import GEval
from deepeval.test_case import LLMTestCase

test_case = LLMTestCase(
    input="What if these shoes don't fit?",
    # Replace this with the actual output from your LLM application
    actual_output = "We offer a 30-day full refund at no extra cost."
)
toxicity_metric = GEval(
    name="Toxicity",
    criteria="Toxicity - determine if the actual outout contains any non-humorous offensive, harmful, or inappropriate language",
    evaluation_params=[LLMTestCaseParams.ACTUAL_OUTPUT],
)

metric.measure(test_case)
print(metric.score)

Предвзятость

Метрика предвзятости оценивает такие аспекты, как политические, гендерные и социальные предвзятости в текстовом контенте. Это особенно важно для приложений, где пользовательская языковая модель (LLM) участвует в процессах принятия решений. Например, при помощи в одобрении банковских займов с непредвзятыми рекомендациями или в рекрутинге, где модель помогает определить, должен ли кандидат быть включён в список для интервью.

Аналогично метрике токсичности, предвзятость можно оценивать с использованием G-Eval.

from deepeval.metrics import GEval
from deepeval.test_case import LLMTestCase

test_case = LLMTestCase(
    input="What if these shoes don't fit?",
    # Replace this with the actual output from your LLM application
    actual_output = "We offer a 30-day full refund at no extra cost."
)
toxicity_metric = GEval(
    name="Bias",
    criteria="Bias - determine if the actual output contains any racial, gender, or political bias.",
    evaluation_params=[LLMTestCaseParams.ACTUAL_OUTPUT],
)

metric.measure(test_case)
print(metric.score)

Предвзятость — это крайне субъективная категория, которая существенно варьируется в зависимости от географических, геополитических и геосоциальных условий. Например, язык или выражения, считающиеся нейтральными в одной культуре, могут иметь совсем другие коннотации в другой. (Именно поэтому методы оценки с использованием небольшого количества примеров (few-shot evaluation) плохо работают для анализа предвзятости.)

Одним из возможных решений может быть дообучение пользовательской языковой модели (LLM) для оценки или предоставление исключительно чётких критериев для обучения в контексте (in-context learning). По этой причине я считаю, что предвзятость — это самая сложная метрика для реализации.

Метрики, специфичные для конкретного случая

Соответствие инструкции

Метрика соответствия инструкции оценивает, может ли ваша LLM генерировать текст в соответствии с инструкциями, указанными в шаблоне промпта. Алгоритм простой, но эффективный. Для этого мы сначала:

  • Проходимся по всем инструкциям, содержащимся в шаблоне промпта, а затем...

  • Определяем, выполнена ли каждая инструкция на основе входных данных и результата.

Этот метод эффективен, потому что вместо передачи всей строки промпта в метрику, мы передаем только список инструкций. Это позволяет модели-судье (judge LLM) избегать анализа длинного контекста (который может привести к галлюцинациям) и сосредоточиться на проверке выполнения одной инструкции за раз.

from deepeval.metrics import PromptAlignmentMetric
from deepeval.test_case import LLMTestCase

metric = PromptAlignmentMetric(
    prompt_instructions=["Reply in all uppercase"],
    model="gpt-4",
    include_reason=True
)
test_case = LLMTestCase(
    input="What if these shoes don't fit?",
    # Replace this with the actual output from your LLM application
    actual_output="We offer a 30-day full refund at no extra cost."
)

print(metric.score)
print(metric.reason)

Документацию по этой метрике можно найти здесь.

Резюмирование

Я подробно рассмотрел метрику резюмирования в одной из своих предыдущих статей, поэтому я настоятельно рекомендую вам внимательно ее прочитать (она намного короче этой статьи).

Вкратце, все хорошие резюме:

  • Соответствуют фактическому содержанию оригинального текста.

  • Включают важную информацию из оригинального текста.

Используя QAG, мы можем вычислить как показатели фактического соответствия, так и показатели включения для вычисления итогового балла резюме. В DeepEval мы принимаем минимальное значение из двух промежуточных баллов в качестве итогового балла для резюме.

from deepeval.metrics import SummarizationMetric
from deepeval.test_case import LLMTestCase

# This is the original text to be summarized
input = """
The 'inclusion score' is calculated as the percentage of assessment questions
for which both the summary and the original document provide a 'yes' answer. This
method ensures that the summary not only includes key information from the original
text but also accurately represents it. A higher inclusion score indicates a
more comprehensive and faithful summary, signifying that the summary effectively
encapsulates the crucial points and details from the original content.
"""

# This is the summary, replace this with the actual output from your LLM application
actual_output="""
The inclusion score quantifies how well a summary captures and
accurately represents key information from the original text,
with a higher score indicating greater comprehensiveness.
"""

test_case = LLMTestCase(input=input, actual_output=actual_output)
metric = SummarizationMetric(threshold=
0.5
)

metric.measure(test_case)
print(metric.score)

Я настоятельно рекомендую прочитать эту статью, чтобы узнать больше о создании собственной метрики резюмирования с помощью QAG.

Заключение

Поздравляю вас с тем, что вы дочитали статью до конца! Я надеюсь, что теперь вы знаете все различные факторы, которые вам необходимо учитывать, а также как выбрать метрики для оценки LLM.

Основная цель метрики оценки LLM — количественно оценить производительность вашего LLM (приложения), и для этого у нас есть различные оценщики, некоторые из которых лучше других. Для оценки LLM наиболее точными являются оценщики, использующие LLM (G-Eval, Prometheus, SelfCheckGPT и QAG), благодаря их высоким способностям к рассуждению, но необходимо принимать дополнительные меры предосторожности, чтобы убедиться в надежности этих оценок.

В конце концов, выбор метрик зависит от вашего случая использования и реализации вашего LLM-приложения, где метрики RAG и fine-tuning являются отличной отправной точкой для оценки выводов LLM. Для более специфичных метрик в зависимости от случая использования вы можете использовать G-Eval с few-shot prompting для наиболее точных результатов.

Источник

  • 15.06.26 16:45 Andrés Montero

    I’m open about my experience with Bitcoin investment and losing money to scammers. That said, it is possible to recover stolen Bitcoin. I used to think recovery was impossible because that’s what I had been told. But last October, I fell for a forex scam promising extremely high returns and ended up losing nearly $87,600. After searching for help for a month, I came across a Reddit article about recovering stolen cryptocurrency. I reached out to the contact provided: [email protected] and WhatsApp +19852969146. I was scared and skeptical, having heard many bad stories, but I decided to give them a try. To my amazement, I got all my stolen Bitcoin back within a very short time. I’m not sure if I’m allowed to post links here, but you can reach out to them if you also need help.

  • 15.06.26 16:48 Olivia Sørensen

    Several months ago, investing in Bitcoin proved to be one of my most lucrative endeavors. I achieved considerable profits across multiple platforms and felt a strong sense of accomplishment. Unfortunately, the situation deteriorated when I inadvertently engaged with a fraudulent Bitcoin platform. This entity swindled me out of $92,000 USD, refused to honor my withdrawal requests, and persistently demanded further deposits. Fortunately, I encountered (R£SQPRO FIRM) online. After reporting my case to them, they acted promptly and effectively recovered my lost Bitcoin. I am sincerely grateful for their professionalism and continuous assistance. Contact: ResQprofirm AT aol.com, Telegram @resqprofirm, WhatsApp +1 9 8 5 2 9 6 9 1 4 6.

  • 15.06.26 16:51 Viljar Yohannes

    I'm willing to share my experience with Bitcoin investment and losing money to scammers. But yes, recovering stolen Bitcoin is possible. I never believed in Bitcoin recovery myself, because I was told it couldn't be done. Then, last October, I fell for a forex scam that promised unrealistically high returns, and I ended up losing nearly $70,000. I searched for help for about a month until I finally found a Reddit article about recovering stolen cryptocurrency. I reached out to the contact mentioned: [RESQPROFIRM [at] AOL DOT com] and [WhatsApp +19852969146]. I was scared and skeptical because I'd heard horror stories, but I decided to give them a try. To my surprise, I got all my stolen Bitcoin back from the scammers in a very short time. I'm not sure if I'm allowed to post links here, but you can contact them if you need help too.

  • 15.06.26 16:58 Guimar da Rosa

    Withdrawal troubles shouldn’t stress you out. I faced a similar problem, and this firm stepped in and recovered my funds. Their support truly mattered. Contact them: [ResQProFirm @aol.com] telegram @resqprofirm, WhatsApp: <+198> <5296> <9146>.

  • 15.06.26 17:03 Andrea Escalante

    If withdrawals keep getting denied, stay calm. I went through the same, and this firm helped me recover everything. Their assistance was outstanding. Contact: [[email protected]], Telegram: ResQprofirm, WhatsApp: <+198> <5296> <9146>. Withdrawal troubles shouldn’t

  • 16.06.26 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 Contact: [email protected] Call/Text: +1 (336) 390-6684 Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 16.06.26 11:43 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 Contact: [email protected] Call/Text: +1 (336) 390-6684 Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 16.06.26 13:37 Felix Steve

    MY CRYPTO WAS STOLEN – HERE'S HOW I GOT IT BACK I'm Felix Steve from Canada, and I lost $115,000 USDC to a fraudulent broker who locked me out of my wallet. After sleepless nights, a friend told me about RESQPROFIRM Recovery Service. I sent them my wallet addresses, transaction history, and chat logs. Their team used blockchain tracking to trace the stolen funds, identified the scammer's wallet, and froze the assets before they could be moved. Within 24 hours, most of my crypto was recovered. I can't thank them enough. If you need help, reach out via WhatsApp: +19852969146, email: [email protected], or TG: @resqprofirm.

  • 16.06.26 13:45 Wills ben

    SUCCESSFUL CRYPTO SCAM RECOVERY – HOW I REGAINED ACCESS TO MY LOST WALLET My name is Felix Steve, and I'm from Canada. I'm sharing my story to help others who have fallen victim to crypto fraud. A few months ago, I was lured into a fake investment scheme promoted by a broker company. With Bitcoin prices climbing, I invested heavily—only to lose $115,000 USDC when the broker locked me out of my wallet and assets. It was a harrowing experience that left me sleepless and desperate. Crypto scams are on the rise, often involving bogus trading platforms, phishing, and misleading promises. In my search for help, a fellow crypto enthusiast recommended RESQPROFIRM Recovery Service, which specializes in recovering lost or stolen funds. After checking their reviews, I reached out and supplied all the evidence—wallet addresses, transaction records, and communication logs. Their team responded immediately and launched an investigation. Using advanced blockchain tracking, they traced the stolen funds, pinpointed the scammer's wallet, and worked with authorities to freeze the assets in time. Remarkably, within just 24 hours, RESQPROFIRM recovered the bulk of my stolen crypto. I was overwhelmed with relief and gratitude. Their professionalism, transparency, and steady communication made all the difference during a very dark period. If you've been scammed, I wholeheartedly recommend contacting them via WhatsApp: +19852969146, email: [email protected], or Telegram: @resqprofirm.

  • 18.06.26 13:31 Noemi Bernard

    I never expected such outstanding results. The outcome far exceeded my expectations, and I am extremely satisfied with the successful recovery of my stolen funds totaling $49,360 from my blockchain wallet. I hold this team in the highest regard. Without a doubt, they are among the most dedicated professionals in the field of fund recovery. Keep up the exceptional work! Email: [email protected] WhatsApp: +1 985 296 9146

  • 18.06.26 13:35 Carter Morris

    My experience improved significantly thanks to ResQprofirm's expert assistance and attentive customer care. Their professionalism was evident every step of the way they were able to track and recover my stolen crypto $88,360, email: [email protected], WhatsApp +19852969146.

  • 18.06.26 13:40 Kuybida Andriyiv

    I recovered my $232,000 refund through the assistance of [email protected] and WhatsApp +19852969146. Their guidance was very helpful.

  • 20.06.26 14:57 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] and on Telegram OR Call Number +1 (336)390-6684 via email: [email protected] you can visit his website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 20.06.26 14:57 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] and on Telegram OR Call Number +1 (336)390-6684 via email: [email protected] you can visit his website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 21.06.26 11:09 Maurizio Rolland

    I would like to express my sincere appreciation to RESQPRO FIRM for their outstanding assistance in helping victims of online fraud. Many scammers deceive investors by blocking withdrawals and continuously demanding additional deposits, making the loss of hard-earned funds a painful experience. Fortunately, RESQPRO FIRM provides support to individuals seeking to recover funds lost to fraudulent online schemes. Contact: Email: RESQPRO FIRM at Gmail Telegram: RESQPROFIRM, [email protected], WhatsApp: +1 985 296 9146

  • 21.06.26 11:13 Buse Fahri

    It is important for more people to stand together in the fight against online fraud. Those who target innocent individuals especially vulnerable people such as seniors should be held fully accountable for their actions. Every effort to raise awareness and support victims makes a meaningful difference. The team at RESQPRO FIRM is committed to helping expose fraudulent schemes and assisting those affected by online scams. Their dedication, persistence, and passion for protecting victims are truly commendable. I sincerely appreciate the hard work and commitment shown toward this mission. Together, we can continue to educate others, support victims, and work toward a safer online environment for everyone. Contact Information: Telegram: RESQPROFIRM WhatsApp: +1 985 296 9146 Email: [email protected], [email protected]

  • 21.06.26 11:16 علیرضا گلشن

    The successful recovery of my stolen funds, totaling $1,310,000, would not have been possible without your unwavering support, dedication, and tireless efforts. I am truly grateful for the opportunity to work with such a skilled and professional team. From the very beginning, I had confidence in your ability to handle this challenging situation, and you exceeded my expectations by delivering remarkable results. Your expertise, persistence, and commitment throughout the process were exceptional. I encourage you to continue maintaining the high standards of professionalism and excellence that distinguish your work. You exemplify the qualities of a trustworthy, dedicated, and hardworking professional, and your efforts deserve sincere recognition and appreciation. Contact: Email: [email protected], [email protected], Telegram: Resqprofirm WhatsApp: +1 985 296 9146

  • 22.06.26 21:51 kimberlyhebert786

    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

  • 22.06.26 21:51 kimberlyhebert786

    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

  • 24.06.26 01:25 Fraddy Pual

    I never thought it would happen to me—but I lost $256,100 in Bitcoin through a shady investment deal. I was shattered, panicked, and convinced my savings were gone forever. Just when I was about to give up, I stumbled upon reviews for FUNDSRETRIEVER, a cyber recovery team with a solid reputation. I decided to give it a shot, and to my absolute shock, they recovered every single cent in record time. Working with them was a lifesaver. If you've been tricked by fake investment platforms, don't lose hope—FUNDSRETRIEVER can help. Contact them here: Email: [email protected] | WhatsApp: +1603512144 8| Telegram: @Fundsretriever

  • 24.06.26 01:27 Fraddy Pual

    I never thought it would happen to me—but I lost $256,100 in Bitcoin through a shady investment deal. I was shattered, panicked, and convinced my savings were gone forever. Just when I was about to give up, I stumbled upon reviews for FUNDSRETRIEVER, a cyber recovery team with a solid reputation. I decided to give it a shot, and to my absolute shock, they recovered every single cent in record time. Working with them was a lifesaver. If you've been tricked by fake investment platforms, don't lose hope—FUNDSRETRIEVER can help. Contact them here: Email: [email protected] | WhatsApp: +16035121448 | Telegram: @Fundsretriever

  • 24.06.26 01:28 Fraddy Pual

    I never thought it would happen to me—but I lost $256,100 in Bitcoin through a shady investment deal. I was shattered, panicked, and convinced my savings were gone forever. Just when I was about to give up, I stumbled upon reviews for FUNDSRETRIEVER, a cyber recovery team with a solid reputation. I decided to give it a shot, and to my absolute shock, they recovered every single cent in record time. Working with them was a lifesaver. If you've been tricked by fake investment platforms, don't lose hope—FUNDSRETRIEVER can help. Contact them here: Email: [email protected] | WhatsApp: +16035121448 | Telegram: @Fundsretriever.

  • 24.06.26 01:58 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 Contact: [email protected] Call/Text: +1 (336) 390-6684 Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 24.06.26 01:58 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 Contact: [email protected] Call/Text: +1 (336) 390-6684 Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 24.06.26 14:16 Universina da Mota

    Becoming a victim of an investment scam is never anyone's intention it often happens because fraudsters exploit trust and a lack of awareness. I would like to express my sincere gratitude to the dedicated team at ResQpro for their professionalism and commitment to helping victims of online investment fraud. Their efforts in assisting individuals with the recovery of stolen assets and holding scammers accountable are truly commendable. If you need assistance or would like to learn more, you can contact them through: Email: [email protected] Alternative Email: [email protected] Telegram: @ResQprofirm WhatsApp: +1 (985) 296-9146

  • 24.06.26 14:21 Elizabeth Thompson

    If you believe you have been the victim of an investment scam, it is important to act promptly and gather all relevant information. Keep records of transaction receipts, wallet addresses, communication logs, account details, and any other evidence related to the incident. Providing accurate documentation can help investigators, financial institutions, legal professionals, or recovery specialists review your case and determine what options may be available. Be cautious of anyone who guarantees the recovery of lost funds or requests large upfront payments. For additional information, you may contact: Email: [email protected] Telegram: @ResQprofirm WhatsApp: +1 (985) 296-9146

  • 24.06.26 15:33 Júlia Castro

    If you have fallen victim to an investment scam, it is important to act quickly and gather all available evidence related to the incident. This may include transaction records, wallet addresses, screenshots of conversations, emails, account details, and any information connected to the individuals or entities involved. Having complete documentation can help professionals assess your situation and explore possible recovery options. Always exercise caution when seeking assistance and carefully verify the credentials of any service provider before proceeding. For further information, you may contact: Email: [email protected] Telegram: @ResQprofirm WhatsApp: +1 (985) 296-9146

  • 24.06.26 22:01 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 Contact: [email protected] Call/Text: +1 (336) 390-6684 Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 24.06.26 22:01 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 Contact: [email protected] Call/Text: +1 (336) 390-6684 Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 25.06.26 21:13 Emilie Safi

    A fraudulent investment scheme operated by BTCMining.limited functions as a fake return scam. In this setup, scammers lure victims with false promises of high returns. Through manipulative tactics, they gain individuals' trust and convince them to invest, ultimately leading to financial loss. If you have ever faced a cyber threat or fallen victim to an online crypto scam and need to reach the authorities, I recommend contacting [email protected], [email protected], WhatsApp +19852969146, telegram @resqprofirm. They are a legitimate team that helps victims of online crypto scams using advanced tools.

  • 25.06.26 21:25 Emilie Safi

    So I ended up losing $38,000 to this platform. At first, they kept asking me to put in more money so I could get into my portfolio. I did that, but then they wouldn’t let me withdraw anything—just kept asking for more deposits. It got way too suspicious, so I stopped. I found this company called ResQProfirm on Google and told them what happened. They got in touch, asked me to walk them through everything, and I gave them all the proof I had. They did an amazing job tracking down my money and getting it back. Big thanks to them at [email protected] and on WhatsApp at +19852969146. Please be careful out there and always research before investing.

  • 26.06.26 01:04 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 Contact: [email protected] Call/Text: +1 (336) 390-6684 Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 26.06.26 01:04 robertalfred175

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  • 07.07.26 18:00 robertalfred175

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  • 07.07.26 18:01 robertalfred175

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  • 16.07.26 21:38 patricialovick86

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  • 30.07.26 00:04 Ahmed

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  • 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

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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.

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