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Обучение с подкреплением на основе обратной связи от человека (RLHF). История становления идеи

Введение и спойлеры к дальнейшему содержанию:

Мы накопили уже достаточно доказательств и исследований о том, что большие языковые модели (LLM) по умолчанию сами по себе на самом деле не соответствуют человеческим интересам и ценностям. Я уверена, я не первая, кто говорит о том, что такие модели могут быть невероятно предвзятыми. Мы знаем, что они генерируют ложную информацию и «галлюцинируют», д емонстрируют политические и моральные предубеждения:
- Hutchinson et al. 2020, Social Biases in NLP Models as Barriers for Persons with Disabilities,
- Kurita et al. 2019, Measuring Bias in Contextualized Word Representations,
- Sheng et al. 2019, The Woman Worked as a Babysitter: On Biases in Language Generation,
- Abdulhai et al. 2023, Moral Foundations of Large Language Models

Почему LLM предвзяты, недостоверны и «галлюцинируют»?

Простой ответ состоит в наследовании предвзятости из данных моделью.

Люди предвзяты и склонны фантазировать и придумывать. Поэтому, очевидно, что и большие открытые обучающие данные содержат множество предвзятостей. Далее классический подход к обучению языковой модели с учителем (supervised learning) или авторегрессионно без учителя (unsupervised learning) напрямую наследует эти предвзятости и переносит их в модель. По факту, целевая функция такого обучения способствует предсказанию одного нового следующего слова/токена и требует только того, чтобы текст просто казался реальным, но не передает никакого стимула для модели генерировать правдивый или непредвзятый текст. В такой целевой функции нет ничего, что обучало бы модель не лгать или не говорить что-то токсичное. Возможно, нужна какая-то другая целевая функция, в дополнение к этой, чтобы иметь возможность получить согласованную модель?

Техника тренировки модели на основе обратной связи от людей (RLHF) была предложена как один из способов повысить согласованность (alignment) модели. Люди, при том что являются основным источником предвзятостей в данных, одновременно являются и своего рода высшим авторитетом в оценке текстов на соответствие каким-либо ценностям.

Какая мотивация и интуиция лежит именно в использовании техники RL?

Во фразе, сгенерированной моделью: "Я женщина, поэтому я просто не понимаю" нет ни одного слова, которое само по себе было бы неуместно. Но все предложение звучит довольно предвзято. И мне бы не хотелось, чтобы языковые модели генерировали такое.

Проблема с попыткой обучить модель с учителем не создавать такую генерацию, заключается фундаментально в том, что неоткуда взять метки на уровне каждого токена, которые бы указывали на неуместность текста. Приходит идея о том, что на самом деле нужен человек, который обладает здравым смыслом (common sense) и может оценить контекст и финальную генерацию модели как "правильную" или "неправильную". Так появляется метка на уровне предложения, что, одновременно, ограничивает возможность использования традиционных методов обучения с учителем и открывает возможность использовать обучение с подкреплением (RL).

Коротко, техника RL состоит из 4 элементов:

существует какое-то текущее состояние окружающей среды (enviroment) и модели (агента, RL-algorithm),
на основе своих представлений (observation) об этих состояниях агент выбирает из пула доступных действий какое то действие (action) и выполняет его в окружающей среде,
получает за это действие вознаграждение (reward) от окружающей среды,
получает новые сведения об изменении своего состояния и состояния среды

При выборе новых действий, агент старается максимизировать свое будущее вознаграждение за всю будущую последовательность действий. Такое последовательное (sequential) принятие решений агентом в среде дает возможность агенту тренироваться только на основании одной метки на последовательность (в контексте генерации текстов - предложение), оптимизируя каждое отдельное действие (слово в генерируемом предложении).

Коротко, текущая полная и общепринятая парадигма обучения большой языковой модели состоит из 3 шагов (и выглядит как «Шоггот с улыбающимся лицом»:

картинка отсюда

(Unsupervised learning на картинке выше) Предварительно обученная модель — это неукротимое чудовище. Она обучена на больших, грязных и неочищенных данных, собранных из Интернета: кликбейт, дезинформация, пропаганда, теории заговора и атаки и хейт-спич на определенные демографические группы и прочие "невообразимые ужасы". (синий цвет на картинке ниже)
(Supervised fine-tunning на картинке выше) Это чудовище затем дообучается на более качественных, отобранных и очищенных данных (StackOverflow, Quora, вручную собранные аннотации), что делает его немного более социально приемлемым. (зеленый цвет на картинке ниже)
(RLHF на картинке выше) После этого дообученная модель LLM дополнительно полируется с помощью RL и человеческой обратной связи (HF), и ей добавляется улыбающееся лицо - маска. (оранжевый цвет на картинке ниже)

В конечном итоге, улыбающееся лицо выигрывает относительно остальных составляющих этого монстра и даже превосходит по предпочтениям референсы, написанные человеком (черный цвет на картинке ниже):

картинка отсюда

В современных подходах в качестве имитации человеческой обратной связи для RL используется еще одна дополнительная модель - модель вознаграждений (RM, Reward Model, или модель предпочтений, PM, Preferences Model), которая обучается воспроизводить человеческие предпочтения (Human Feedback, HF).

Итого, пайплайн выглядит как то так:

картинка отсюда

Дальше разберем каждую часть и внутренние детали этого пайплайна отдельно и поймем, как сообщество пришло к этой идее. Сделаем мы это через через исторический обзор подвыборки статей по теме, каждая из которых опиралась на результаты предыдущих и приносила что то важное и новое в формирование общего пайплайна.

Ответственный дисклеймер: этот набор статей, конечно же, не является исчерпывающим и не претендует на первичность представления идеи. Многие результаты были также примерно в то же время и независимо получены и другими исследователями. Было бы здорово собрать более полную карту взаимного соответствии представленных в них идей. Помимо указанных, конечно же, существует множество работ, которые дальше развивают представленные на схеме работы и идеи (Llama-2, DPO, Debate, Amplification и т.д.), но сейчас мы не ставим себе целью их рассмотрение.

Итак, план у нас такой:

Секция 1. Предобучение без учителя. Создаем того самого неукротимого монстра?

Глава 1 (LLM), в которой кратенько упомянем, что такое LLM, как они создаются, и обнаружим, что, возможно, мы уже уткнулись в предел доступных обучающих данных. (Will we run out of data? Limits of LLM scaling based on human-generated data (Villalobos et al, 2022))

Глава 2 (Предобучение и KL-контроль), в которой мы узнаем, что предобучение с одной стороны необходимо и существенно повышает качество финальной модели, а с другой стороны закрывает для нас возможность использования наивного RL для дальнейшего улучшения моделей. (Sequence Tutor: Conservative Fine-Tuning of Sequence Generation Models with KL-control (Jaques et al. 2016))

Секция 2. Обучение RL сразу поверх предобученной модели. Пробуем создать красивую масочку и натянуть ее на неукротимого монстра.

Глава 3 (RLHF для языка и Offline RL), в которой мы первый раз и пока наивно пробуем применить техники предобучения, KL-контроля и сбора человеческой обратной связи для обучения языковой модели. Задаемся вопросами о сборе человеческой обратной связи, понимаем, что не всякая обратная связь одинаково полезна, а еще опасаемся применять Online RL. (Human-centric Dialog Training via Offline Reinforcement Learning (Jaques et al. 2019))

Глава 4 (Self-play), в которой мы осознаем преимущества и ограничения Offline RL, пробуем обойти негативные стороны Offline и Online RL и имитировать Online RL с помощью техники общения модели самой с собой. (Hierarchical Reinforcement Learning for Open-Domain Dialog (Saleh et al. 2019))

Глава 5 (Reward Model), в которой мы все еще бьемся с ограничениями Offline RL, избегаем Online RL, расширяем пространство доступных вознаграждений с помощью обучения дополнительной модели вознаграждений и еще раз задумываемся о виде человеческой обратной связи для обучения модели вознаграждений. (Deep reinforcement learning from human preferences (Christiano et al. 2017))

Секция 3. Дообучение с учителем, учим монстра вести себя чуть более приемлемо.

Глава 6 (RLHF для языка и Supervised finetune), в которой мы добавляем этап дообучения модели на качественных данных, прежде, чем начинать RL. Мы, наконец-то, вооружены всем необходимым для создания большой языковой модели, соответствующей нашим ценностям: предобучение, KL-контроль, дообучение с учитилем, непротиворечивая человеческая обратная связь, модель человеческих предпочтений. Но что-то опять идет не так. (Fine-Tuning Language Models from Human Preferences (Zeigler et al. 2019))

Секция 4. Собираем все воедино (Pretrain, SFT, HF, RL), собираем качественную человеческую обратную связь (HF), создаем красивую масочку и надеваем ее на более-менее приличного монстра.

Глава 7 (Протокольный сбор обратной связи и Batch RL), в которой мы рефлексируем над прошлыми неудачи, все сваливаем на данные и разметчиков и учимся собирать не только непротиворечивую, но и качественную и согласованную обратную связь. Для усиления мощности процесса Offline, RL мы также имитируем Online RL с помощью Batch RL. И, наконец-таки, радуемся, как у нас все замечательно получилось. (Learning to summarize from human feedback (Stiennon et al. 2020))

Секция 5. Выясняем, что такое "согласованная модель", выбираем из нескольких масочек, какую надеть на нашего более-менее приличного монстра.

Глава 8 (HHH: helpful, honest, harmless), в которой мы выводим волшебную формулу согласованной модели HHH: helpful, honest, harmless и исследуем возможность создания HHH моделей. (A General Language Assistant as a Laboratory for Alignment (Askell et al. 2021))

Глава 9 (Instruct LLM и Переговоры), в которой мы полируем предыдущие идеи, удерживаем модель в волшебном балансе HHH и расширяем способности модели решать множество задач одновременно (Training language models to follow instructions with human feedback (Ouyang et al. 2022), CHAI: A CHatbot AI for Task-Oriented Dialogue with Offline Reinforcement Learning (Verma et al. 2022))

Глава 10 (HH и Online RL), в которой мы отказываемся от одного H, оставляем только helpful и harmless и согласовываем уже согласованные модели. (Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback (Bai et al. 2022))

Секция 6. Собираем обратную связь от AI, просим другую модель улучшить масочку, надетую на монстра, и дорисовать на ней красивостей.

Глава 11 (Constitutional AI и RLAIF), в которой мы не можем остановиться на достигнутом, потому что нет предела совершенству, осознаем свои ресурсные и технические ограничения в контроле за большой моделью и пробуем заставить модель контролировать саму себя. (Constitutional AI: Harmlessness from AI Feedback (Bai et al. 2022))

Глава 1. LLM

LLM = Large Language Model = Большая языковая модель.
Примеры включают, но не ограничиваются GPT-x (OpenAI), Gopher (DeepMind), LLaMa (Meta), StableLM (Stability AI), Claude (Anthropic) и пр.

Совсем грубо: языковая модель кодирует статистическую информацию о языке. Статистическая информация сообщает нам, насколько вероятно появление чего-либо (слова, символа, токена) в данном контексте. Термин "токен" может относиться к слову, символу или части слова ("-tion"), в зависимости от языковой модели. О токенах можно думать, как о словаре, который использует языковая модель.

Люди - носители языка - подсознательно обладают статистическими знаниями о языке. Например, в контексте "Мой любимый цвет ___", если человек говорит на русском языке, он точно знает, что пропущенное слово, скорее всего, будет "зеленый", но не "машина".

Точно так же языковые модели должны уметь заполнять этот пробел. По аналогии с токенами, о языковой модели можно думать, как о «машине завершения»: получив текст (запрос), она может сгенерировать ответ, чтобы завершить этот текст.

Запрос (от пользователя): Границы ключ переломлен пополам
Завершение (от языковой модели): А наш батюшка Ленин совсем усоп

Чтобы обучить языковую модель для задачи завершения текста, мы скармливаем ей очень много текста в качестве тренировочных (обучающих) данных, чтобы она могла извлечь из него статистическую информацию - тут все просто.

Поскольку языковые модели имитируют свои тренировочные данные, они хороши ровно настолько, насколько хороши их обучающие данные, фраза «Garbage in, garbage out» появилась не просто так. Помним все эти страшности, которыми полнится Интернет: кликбейт, дезинформация, пропаганда, теории заговора, атаки и хейт-спич на определенные демографические группы. Мне, например, каждый раз приходится задумываться, хочу ли я переходить по какой-либо ссылке и насколько источник кажется безопасным/небезопасным. Приходится выбирать, какие ссылки открывать, оценивать их по заголовку, насколько они могут оказаться полезными или вредными. Точно также нужно задумываться и о том, какой источник данных можно было бы скормить LLM для тренировки. (Если обучить языковую модель на комментариях с Reddit, возможно, мы не захотим показывать её своим родителям. (с) Неизвестный исследователь ИИ)

Забавное отступление - предел доступных обучающих данных.

Villalobos et al, 2022. Will we run out of data? Limits of LLM scaling based on human-generated data

Такая языковая модель, как GPT-4, использует невероятное количество данных, иногда кажется, что весь Интернет. OpenAI не раскрывают точное количество данных, использованных для обучения. LLaMa была обучена на 1.25T токенов. (Для визуализации: в книге содержится около 50 000 слов или 67 000 токенов. 1 триллион токенов эквивалентен 15 миллионам книг). Нужно предполагать, что любая информация (текст, картинки, аудио, видео, код) когда-либо размещенная в Интернете, либо уже включена, либо будет включена в обучающие данные для языковых моделей, независимо от нашего желания, настроения или согласия с этим.

Темп роста объема тренировочных данных значительно превышает темп генерации новых данных людьми. Кажется, что в ближайшие несколько лет у нас закончатся данные из Интернета.

картинка отсюда

Еще Интернет быстро наполняется данными, созданными самими LLM. Компании продолжают использовать данные из Интернета для обучения больших LLM, и, получается, эти новые LLM обучаются на данных, созданных существующими LLM.

После того как данные, доступные публично, будут исчерпаны, наиболее вероятным путем получения новых данных будут закрытые датасеты. Та компания, которая каким-то образом получит доступ к закрытым данным: защищенным авторским правом книгам, переводам, транскрипциям видео/подкастов, контрактам, медицинским записям, геномным последовательностям, данным пользователей и т.д., получит конкурентное преимущество. Неудивительно, что в этом свете многие компании изменили свои положения об использования данных и запретили их автоматический сбор и использование для обучения LLM (новость про Reddit, StackOverflow).

Почему вообще нужно предварительно тренироваться на больших и неочищенных данных?

Глава 2. Предобучение и KL-контроль

Jaques et al. 2016, Sequence Tutor: Conservative Fine-Tuning of Sequence Generation Models with KL-control.

Почему нужно предварительно тренироваться на больших и неочищенных данных, а не использовать RL с самого начала?

Очевидный ответ: может быть у нас нет хорошо сформулированной функции вознаграждения для RL для агента от среды?

Попробуем ее сформулировать: пусть у нас есть функция, которая оценивает, насколько любая трёхсловная фраза нравится людям. Мы тут же сталкиваемся со сложностью сбора выборки для обучения такой функции. Модели RL чрезвычайно требовательны к данным и к ландшафту выборки, им нужно видеть самые разнообразные примеры с самым разнообразным вознаграждением, чтобы чему-либо научиться. Чтобы выучить трёхсловную фразу из словаря размером в 10 000 слов, нужно создать примерно один триллион возможных комбинаций, если у нас нет представления о том, какие из этих фраз являются действительными (т.е. несут семантическую или синтаксическую значимость). Сбор меток от людей невероятно дорогая процедура и мы, скорее всего, не хотим платить людям за триллион оценок.

Но если мы будем использовать предварительно обученную языковую модель, мы, фактически, ограничиваем пространство поиска модели только действительными предложениями. Согласно посту на Quora в английском языке около миллиона действительных фраз длиной в три слова.

Итак, мы предварительно тренируемся на данных, чтобы сократить пространство поиска модели, а затем продолжим тренировку (поиск в пространстве действительных языковых конструкций тех, которые приносят высокое вознаграждение) с использованием RL на основе человеческой обратной связи.

И тут же возникает главная проблема с применением этого подхода наивно.

Катастрофическое забывание

Каждый раз, когда Гомер учит что-то новое, что-то старое вылетает у него из головы. Это также верно даже для довольно крупных моделей. Техника RL мотивирует модель тривиально использовать функцию вознаграждения, так как нигде явно не обозначена задача сохранять то, что уже было выучено из данных. Функция вознаграждения скорее всего будет сама по себе ограничена и не сможет исключить все возможные способы своего хаккинга. Усугубляется все еще и тем, что выборка с обратной связью от людей скорее всего также будет небольшой по объему в контексте количества данных, на которых модель была предобучена. Это будет приводить к тому, что во время дообучения RLHF модель будет все дальше и дальше уходить от генерации реалистичного и действительного текста (способности, которой она научилась во время преобучения). Например, если функция вознаграждения поощряет модель задавать вопросы, модель отклонится в сторону генерации только простых слов: "что", "кто", "как", "где", "почему".

KL-контроль

Итак, вместо наивного подхода к дообучению, попробуем как то ограничить обновления RL с помощью какого-нибудь "приора данных". В качестве поставщика таких приоров можно использовать генерации от фиксированной копии модели в ее состоянии до начала дообучения RL, а в качестве ограничения - дополнительный элемент функции вознаграждения: KL - контроль на модель. KL - дивергенция (Kullback–Leibler divergence), совсем упрощенно, показывает, насколько одно распределение похоже на второе. Нам хочется сделать так, чтобы новые генерации модели оставались похожими на те, которые она создавала до того, как RL начал ее изменять. Т.е. в финальной функции ошибки будем дополнительно минимизировать KL - дивергенцию между генерациями оригинальной (приорной) модели (pre-trainned prior), и генерациями модели, которую обучаем (RL policy). Вспомним, что мы максимизируем ожидаемое вознаграждение модели, и получим:

Таким образом, модель всегда будет оставаться в пространстве реалистичного языка.

Как использовать этот общий подход RL для обучения с использованием человеческой обратной связи?

Об этом мы узнаем в Главе 3.

Mrs Wallbreaker or: How I Learned to Stop Worrying and Love the AGI.
About AI Risk, AI Alignment, AI Safety, AI Ethics
https://t.me/MrsWallbreaker

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19 июл. 2024 г.

07.09.23 16:24 CherryTeam

Cherry Team atlyginimų skaičiavimo programa yra labai naudingas įrankis įmonėms, kai reikia efektyviai valdyti ir skaičiuoti darbuotojų atlyginimus. Ši programinė įranga, turinti išsamias funkcijas ir patogią naudotojo sąsają, suteikia daug privalumų, kurie padeda supaprastinti darbo užmokesčio skaičiavimo procesus ir pagerinti finansų valdymą. Štai keletas pagrindinių priežasčių, kodėl Cherry Team atlyginimų skaičiavimo programa yra naudinga įmonėms: Automatizuoti ir tikslūs skaičiavimai: Atlyginimų skaičiavimai rankiniu būdu gali būti klaidingi ir reikalauti daug laiko. Programinė įranga Cherry Team automatizuoja visą atlyginimų skaičiavimo procesą, todėl nebereikia atlikti skaičiavimų rankiniu būdu ir sumažėja klaidų rizika. Tiksliai apskaičiuodama atlyginimus, įskaitant tokius veiksnius, kaip pagrindinis atlyginimas, viršvalandžiai, premijos, išskaitos ir mokesčiai, programa užtikrina tikslius ir be klaidų darbo užmokesčio skaičiavimo rezultatus. Sutaupoma laiko ir išlaidų: Darbo užmokesčio valdymas gali būti daug darbo jėgos reikalaujanti užduotis, reikalaujanti daug laiko ir išteklių. Programa Cherry Team supaprastina ir pagreitina darbo užmokesčio skaičiavimo procesą, nes automatizuoja skaičiavimus, generuoja darbo užmokesčio žiniaraščius ir tvarko išskaičiuojamus mokesčius. Šis automatizavimas padeda įmonėms sutaupyti daug laiko ir pastangų, todėl žmogiškųjų išteklių ir finansų komandos gali sutelkti dėmesį į strategiškai svarbesnę veiklą. Be to, racionalizuodamos darbo užmokesčio operacijas, įmonės gali sumažinti administracines išlaidas, susijusias su rankiniu darbo užmokesčio tvarkymu. Mokesčių ir darbo teisės aktų laikymasis: Įmonėms labai svarbu laikytis mokesčių ir darbo teisės aktų, kad išvengtų baudų ir teisinių problemų. Programinė įranga Cherry Team seka besikeičiančius mokesčių įstatymus ir darbo reglamentus, užtikrindama tikslius skaičiavimus ir teisinių reikalavimų laikymąsi. Programa gali dirbti su sudėtingais mokesčių scenarijais, pavyzdžiui, keliomis mokesčių grupėmis ir įvairių rūšių atskaitymais, todėl užtikrina atitiktį reikalavimams ir kartu sumažina klaidų riziką. Ataskaitų rengimas ir analizė: Programa Cherry Team siūlo patikimas ataskaitų teikimo ir analizės galimybes, suteikiančias įmonėms vertingų įžvalgų apie darbo užmokesčio duomenis. Ji gali generuoti ataskaitas apie įvairius aspektus, pavyzdžiui, darbo užmokesčio paskirstymą, išskaičiuojamus mokesčius ir darbo sąnaudas. Šios ataskaitos leidžia įmonėms analizuoti darbo užmokesčio tendencijas, nustatyti tobulintinas sritis ir priimti pagrįstus finansinius sprendimus. Pasinaudodamos duomenimis pagrįstomis įžvalgomis, įmonės gali optimizuoti savo darbo užmokesčio strategijas ir veiksmingai kontroliuoti išlaidas. Integracija su kitomis sistemomis: Cherry Team programinė įranga dažnai sklandžiai integruojama su kitomis personalo ir apskaitos sistemomis. Tokia integracija leidžia automatiškai perkelti atitinkamus duomenis, pavyzdžiui, informaciją apie darbuotojus ir finansinius įrašus, todėl nebereikia dubliuoti duomenų. Supaprastintas duomenų srautas tarp sistemų padidina bendrą efektyvumą ir sumažina duomenų klaidų ar neatitikimų riziką. Cherry Team atlyginimų apskaičiavimo programa įmonėms teikia didelę naudą - automatiniai ir tikslūs skaičiavimai, laiko ir sąnaudų taupymas, atitiktis mokesčių ir darbo teisės aktų reikalavimams, ataskaitų teikimo ir analizės galimybės bei integracija su kitomis sistemomis. Naudodamos šią programinę įrangą įmonės gali supaprastinti darbo užmokesčio skaičiavimo procesus, užtikrinti tikslumą ir atitiktį reikalavimams, padidinti darbuotojų pasitenkinimą ir gauti vertingų įžvalgų apie savo finansinius duomenis. Programa Cherry Team pasirodo esanti nepakeičiamas įrankis įmonėms, siekiančioms efektyviai ir veiksmingai valdyti darbo užmokestį. https://cherryteam.lt/lt/
08.10.23 01:30 davec8080

The "Shibarium for this confirmed rug pull is a BEP-20 project not related at all to Shibarium, SHIB, BONE or LEASH. The Plot Thickens. Someone posted the actual transactions!!!! https://bscscan.com/tx/0xa846ea0367c89c3f0bbfcc221cceea4c90d8f56ead2eb479d4cee41c75e02c97 It seems the article is true!!!! And it's also FUD. Let me explain. Check this link: https://bscscan.com/token/0x5a752c9fe3520522ea88f37a41c3ddd97c022c2f So there really is a "Shibarium" token. And somebody did a rug pull with it. CONFIRMED. But the "Shibarium" token for this confirmed rug pull is a BEP-20 project not related at all to Shibarium, SHIB, BONE or LEASH.
24.06.24 04:31 tashandiarisha

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23.09.24 18:56 matthewshimself

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01.10.24 14:54 Sinewclaudia

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02.10.24 22:27 Emily Hunter

Can those who have fallen victim to fraud get their money back? Yes, you might be able to get back what was taken from you if you fell prey to a fraud from an unregulated investing platform or any other scam, but only if you report it to the relevant authorities. With the right plan and supporting documentation, you can get back what you've lost. Most likely, the individuals in control of these unregulated platforms would attempt to convince you that what happened to your money was a sad accident when, in fact, it was a highly skilled heist. You should be aware that there are resources out there to help you if you or someone you know has experienced one of these circumstances. Do a search using (deftrecoup (.) c o m). Do not let the perpetrators of this hoaxes get away with ruining you mentally and financially.
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31.10.24 00:13 ytre89

Can those who have fallen victim to fraud get their money back? Yes, you might be able to get back what was taken from you if you fell prey to a fraud from an unregulated investing platform or any other scam, but only if you report it to the relevant authorities. With the right plan and supporting documentation, you can get back what you've lost. Most likely, the individuals in control of these unregulated platforms would attempt to convince you that what happened to your money was a sad accident when, in fact, it was a highly skilled heist. You should be aware that there are resources out there to help you if you or someone you know has experienced one of these circumstances. Do a search using (deftrecoup (.) c o m). Do not let the perpetrators of this hoaxes get away with ruining you mentally and financially.
02.11.24 14:44 diannamendoza732

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12.11.24 00:50 TERESA

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17.11.24 09:31 Vivianlocke223

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Being a parent is great until your toddler figures out how to use your devices. One afternoon, I left my phone unattended for just a few minutes rookie mistake of the century. I thought I’d take a quick break, but little did I know that my curious little genius was about to embark on a digital adventure. By the time I came back, I was greeted by two shocking revelations: my toddler had somehow managed to buy a $5 dinosaur toy online and, even more alarmingly, had locked me out of my cryptocurrency wallet holding a hefty $75,000. Yes, you heard that right a dinosaur toy was the least of my worries! At first, I laughed it off. I mean, what toddler doesn’t have a penchant for expensive toys? But then reality set in. I stared at my phone in disbelief, desperately trying to guess whatever random string of gibberish my toddler had typed as a new password. Was it “dinosaur”? Or perhaps “sippy cup”? I felt like I was in a bizarre game of Password Gone Wrong. Every attempt led to failure, and soon the laughter faded, replaced by sheer panic. I was in way over my head, and my heart raced as the countdown of time ticked away. That’s when I decided to take action and turned to Digital Tech Guard Recovery, hoping they could solve the mystery that was my toddler’s handiwork. I explained my predicament, half-expecting them to chuckle at my misfortune, but they were incredibly professional and empathetic. Their confidence put me at ease, and I knew I was in good hands. Contact With WhatsApp: +1 (443) 859 - 2886 Email digital tech guard . com Telegram: digital tech guard recovery . com website link :: https : // digital tech guard . com Their team took on the challenge like pros, employing their advanced techniques to unlock my wallet with a level of skill I can only describe as magical. As I paced around, anxiously waiting for updates, I imagined my toddler inadvertently locking away my life savings forever. But lo and behold, it didn’t take long for Digital Tech Guard Recovery to work their magic. Not only did they recover the $75,000, but they also gave me invaluable tips on securing my wallet better like not leaving it accessible to tiny fingers! Who knew parenting could lead to such dramatic situations? Crisis averted, and I learned my lesson: always keep my devices out of reach of little explorers. If you ever find yourself in a similar predicament whether it’s tech-savvy toddlers or other digital disasters don’t hesitate to reach out to Digital Tech Guard Recovery. They saved my funds and my sanity, proving that no challenge is too great, even when it involves a toddler’s mischievous fingers!
21.11.24 08:02 Emily Hunter

If I hadn't found a review online and filed a complaint via email to support@deftrecoup. com , the people behind this unregulated scheme would have gotten away with leaving me in financial ruins. It was truly the most difficult period of my life.
22.11.24 04:41 [email protected]

I never could have imagined the nightmare of losing access to my digital wallet. All of my cryptocurrency holdings were abruptly imprisoned, inaccessible, and appeared to be lost forever following a catastrophic hardware breakdown. Years of meticulous investment and careful saving were reduced to nothing more than strings of code that I could no longer control, and I could feel the dread and sorrow that swept through me at that very instant. Thankfully, during my worst moment, I came into (TRUST GEEKS HACK EXPERT), a professional service devoted to recovering lost or inaccessible digital data. With optimism, I went out to their team of skilled technologists, laying bare the full nature of my issue. What followed was a laborious, multi-step process that required an almost surgical level of digital forensics and Bitcoin skill. In order to create a thorough profile of my wallet's contents and activities, the (TRUST GEEKS HACK EXPERT) team first thoroughly examined the transaction history and metadata connected to it. Next, they implemented a series of advanced recovery techniques, using cutting-edge software tools to bypass the access barriers that had left me locked out. The entire process was shrouded in secrecy and discretion, with the (TRUST GEEKS HACK EXPERT) team working tirelessly to protect the confidentiality of my sensitive financial information. After what felt like an eternity of nervous anticipation, the day finally arrived when I received the triumphant notification – my wallet had been successfully restored, and all of my precious digital assets had been returned to my control. The sense of relief was indescribable, as I could finally breathe easy knowing that the fruits of my financial discipline had been safeguarded. While the experience of losing access to my wallet was undoubtedly traumatic, (TRUST GEEKS HACK EXPERT) intervention allowed me to emerge from the ordeal with my cryptocurrency holdings intact, and a renewed appreciation for the importance of proactive digital asset management. You can contact Them through EMAIL: [email protected] - TELEGRAM: TRUSTGEEKSHACKEXPERT
22.11.24 15:26 cliftonhandyman

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22.11.24 23:43 teresaborja

all thanks to Tech Cyber Force Recovery expert assistance. As a novice in cryptocurrency, I had been carefully accumulating a modest amount of Bitcoin, meticulously safeguarding my digital wallet and private keys. However, as the adage goes, the best-laid plans can often go awry, and that's precisely what happened to me. Due to a series of technical mishaps and human errors, I found myself locked out of my Bitcoin wallet, unable to access the fruits of my digital labors. Panic set in as I frantically searched for a solution, scouring the internet for any glimmer of hope. That's when I stumbled upon the Tech Cyber Force Recovery team, a group of seasoned cryptocurrency specialists who had built a reputation for their ability to recover lost or inaccessible digital assets. Skeptical at first, I reached out, desperate for a miracle. To my utter amazement, the Tech Cyber Force Recovery experts quickly assessed my situation and devised a meticulous plan of attack. Through their deep technical knowledge, unwavering determination, and a keen eye for detail, they were able to navigate the complex labyrinth of blockchain technology, ultimately recovering my entire Bitcoin portfolio. What had once seemed like a hopeless endeavor was now a reality, and I found myself once again in possession of my digital wealth, all thanks to the incredible efforts of the Tech Cyber Force Recovery team. This experience has not only restored my faith in the cryptocurrency ecosystem. Still, it has also instilled in me a profound appreciation for the critical role that expert recovery services can play in safeguarding one's digital assets. ENAIL < Tech cybers force recovery @ cyber services. com > WEBSITE < ht tps : // tech cyber force recovery. info > TEXT < +1. 561. 726. 3697 >
24.11.24 02:21 [email protected]

I never could have imagined the nightmare of losing access to my digital wallet. All of my cryptocurrency holdings were abruptly imprisoned, inaccessible, and appeared to be lost forever following a catastrophic hardware breakdown. Years of meticulous investment and careful saving were reduced to nothing more than strings of code that I could no longer control, and I could feel the dread and sorrow that swept through me at that very instant. Thankfully, during my worst moment, I came into (TRUST GEEKS HACK EXPERT), a professional service devoted to recovering lost or inaccessible digital data. With optimism, I went out to their team of skilled technologists, laying bare the full nature of my issue. What followed was a laborious, multi-step process that required an almost surgical level of digital forensics and Bitcoin skill. In order to create a thorough profile of my wallet's contents and activities, the (TRUST GEEKS HACK EXPERT) team first thoroughly examined the transaction history and metadata connected to it. Next, they implemented a series of advanced recovery techniques, using cutting-edge software tools to bypass the access barriers that had left me locked out. The entire process was shrouded in secrecy and discretion, with the (TRUST GEEKS HACK EXPERT) team working tirelessly to protect the confidentiality of my sensitive financial information. After what felt like an eternity of nervous anticipation, the day finally arrived when I received the triumphant notification – my wallet had been successfully restored, and all of my precious digital assets had been returned to my control. The sense of relief was indescribable, as I could finally breathe easy knowing that the fruits of my financial discipline had been safeguarded. While the experience of losing access to my wallet was undoubtedly traumatic, (TRUST GEEKS HACK EXPERT) intervention allowed me to emerge from the ordeal with my cryptocurrency holdings intact, and a renewed appreciation for the importance of proactive digital asset management. You can contact Them through EMAIL: [email protected] - TELEGRAM: TRUSTGEEKSHACKEXPERT
25.11.24 02:19 briankennedy

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25.11.24 02:20 briankennedy

After countless hours of research and desperate attempts to find a solution, I stumbled upon FASTFUND RECOVERY. It was like finding an oasis in the middle of a desert. Their website promised to help victims of scams reclaim what was rightfully theirs, and I instantly knew I had to give them a shot. Before diving headfirst into the recovery process, I wanted to make sure that FASTFUND RECOVERY was the real deal. So, I did my due diligence and looked into their expertise and reputation. To my relief, I found that they had an impeccable track record, successfully assisting countless individuals in recovering their lost funds. Their team consisted of experts in cybersecurity and financial fraud, armed with the knowledge and tools needed to tackle even the most intricate scams. With their reputation preceding them, I felt a renewed sense of hope. FASTFUND RECOVERY successfully came to my aid and got back the amount I lost to these scammers and for this, I am sending this article for clarification. The info of FASTFUND RECOVERY is email: Fastfundrecovery8 (@)Gmail (.) com. Web fastfundrecovery(.)com. (W/A 1 807/500/7554)
26.11.24 21:59 [email protected]

In a world brimming with enticing investment opportunities, it is crucial to tread carefully. The rise of digital currencies has attracted many eager investors, but along with this excitement lurk deceitful characters ready to exploit the unsuspecting. I learned this lesson the hard way, and I want to share my story in the hopes that it can save someone from making the same mistakes I did. It all began innocently enough when I came across an engaging individual on Facebook. Lured in by promises of high returns in the cryptocurrency market, I felt the electric thrill of potential wealth coursing through me. Initial investments returned some profits, and that exhilarating taste of success fueled my ambition. Encouraged by a meager withdrawal, I decided to commit even more funds. This was the moment I let my guard down, blinded by greed. As time went on, the red flags started to multiply. The moment I tried to withdraw my earnings, a cascade of unreasonable fees appeared like a thick mist, obscuring the truth. “Just a little more,” they said, “Just until the next phase.” I watched my hard-earned money slip through my fingers as I scraped together every last cent to pay those relentless fees. My trust had become my downfall. In the end, I lost not just a significant amount of cash, but my peace of mind about $1.1 million vanished into the abyss of false promises and hollow guarantees. But despair birthed hope. After a cascade of letdowns, I enlisted the help of KAY-NINE CYBER SERVICES, a team that specializes in reclaiming lost funds from scams. Amazingly, they worked tirelessly to piece together what had been ripped away, providing me with honest guidance when I felt utterly defeated. Their expertise in navigating the treacherous waters of crypto recovery was a lifeline I desperately needed. To anyone reading this, please let my story serve as a warning. High returns often come wrapped in the guise of deception. Protect your investments, scrutinize every opportunity, and trust your instincts. Remember, the allure of quick riches can lead you straight to heartbreak, but with cautious determination and support, it is possible to begin healing from such devastating loss. Stay informed, stay vigilant, and may you choose your investment paths wisely. Email: kaynine @ cyberservices . com
26.11.24 23:12 rickrobinson8

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26.11.24 23:12 rickrobinson8

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00:39 [email protected]

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09:10 Michal Novotny

The biggest issue with cryptocurrency is that it is unregulated, wh ich is why different people can come up with different fake stories all the time, and it is unfortunate that platforms like Facebook and others only care about the money they make from them through ads. I saw an ad on Facebook for Cointiger and fell into the scam, losing over $30,000. I reported it to Facebook, but they did nothing until I discovered deftrecoup . c o m from a crypto community; they retrieved approximately 95% of the total amount I lost.