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Действительно ли большие языковые модели галлюцинируют? Эксперимент

Существует мнение, что основная проблема больших языковых моделей — в склонности к галлюцинациям. Когда нейросеть генерирует текст с информацией, не связанной с запросом.

Меня зовут Полина, я инженер по разработке ПО искусственного интеллекта в YADRO. Вместе с коллегами я разрабатываю системы на базе генеративных моделей, в том числе вопросно-ответных ассистентов. В рамках одного из проектов мы вместе с экспертом команды Андреем Соколовым задались вопросом: действительно ли проблема галлюцинаций так актуальна для современных предобученных LLM в вопросно-ответном сценарии.

Для этого мы провели эксперимент на собранном нами датасете. Попутно рассказали про модели-трансформеры и дали строгое определение понятию «галлюцинации LLM», которое повлияло на чистоту эксперимента.

Коротко про трансформеры

Сложно встретить человека, который бы не слышал про большие языковые модели, такие как GPT-4, LLAMA и подобные. В их основе лежат нейросетевые модели, построенные по архитектуре, которая называется трансформер. После открытия она стала невероятно успешной во множестве задач машинного обучения:

обучение представлений (representation learning). Модель BERT, представленная в 2018 году, подняла планку соревнования GLUE (сборник задач на понимание естественного языка) до 80% — прирост составил 8%.
обучение без учителя многозадачных NLP-моделей, способных решать одновременно множество задач по обработке естественного языка. Так, GPT-2, обученная в режиме «без учителя» на множестве общедоступных текстов, продемонстрировала близкие к рекордным результаты в таких задачах, как перевод, суммаризация, ответ на вопрос и других. При этом во время обучения модель не видела ни одного примера из датасетов, соответствующих задачам.
классификация изображений. Модель Vision Transformer (ViT), представленная в 2020 году, достигла качества распознавания на датасетах ImageNet, CIFAR и других, сопоставимого с лучшими на тот момент моделями на базе сверточных нейросетей.

Сейчас трудно найти задачу машинного обучения, для которой в лидерах не было бы трансформерных моделей. Однако у них есть и существенные недостатки. В частности, некоторые из моделей могут галлюцинировать, то есть генерировать информацию, никак не связанную с запросом.

Чтобы понять, как возникают галлюцинации, необходимо немного погрузиться в архитектуру трансформерных моделей.

Обзор архитектуры трансформерной модели

Ниже на рисунке — общий вид трансформерной модели, которая преобразует текст в текст. Классический пример такой задачи — машинный перевод.

Модель состоит из двух частей: энкодера и декодера. Пример полной трансформерной модели: BART.

Задача энкодера — в том, чтобы для поданного на вход набора токенов (элементарных частей текста) $a_{1},...,a_{n}$ получить набор их контекстно-зависимых представлений, или векторов эмбеддингов, — $b_{1},...,b_{n}$ . При этом количество входных токенов и выходных векторов эмбеддингов совпадают.

Декодер работает по тактам. На очередном k-м такте декодер принимает на вход набор контекстно-зависимых представлений из энкодера, а также последовательность токенов входных символов — $s_{1},...,s_{k-1}$ . При этом $s_{1},...,s_{k-1}$ представляет собой последовательность символов, которые были сгенерированы декодером за первые k-1 тактов. Далее декодер для каждого возможного токена из словаря вычисляет вероятность его появления на текущем -м такте — $p_{1},...,p_{n}$ . Здесь — общее количество токенов в словаре.

Энкодер и декодер могут использоваться независимо:

Если нет декодера, результатом модели являются векторы контекстно-зависимых представлений. Их можно использовать для поиска информации, понимания языка и других. Примеры encoder-only моделей — BERT, RoBERTa и подобные.
Если нет энкодера, входная информация в модель подается только в виде начальной текстовой затравки (промпта) — последовательности токенов $s_{1},...,s_{j}$ . Исследования показывают, что во многих прикладных задачах decoder-only модели достигают качества, сопоставимого с полными трансформерными моделями, где есть и энкодер, и декодер. Пример decoder-only модели — GPT-2.

Авторегрессионная генерация

Декодер не умеет выбирать очередной выходной символ энкодера. За это отвечает отдельный алгоритм семплирования (или стратегия семплирования), который, вообще говоря, не является частью модели. На практике применяются различные алгоритмы, которые выбирают случайным образом один токен из нескольких наиболее вероятных. Выбранный токен добавляется в конец последовательности выходных символов $s_{1},...,s_{k}$ , и процедура генерации очередного токена повторяется.

Процесс генерации, который называется авторегрессионным, завершается в двух случаях:

на очередном такте будет выбран символ конца предложения (в словаре ему соответствует специальный токен),
или будет достигнуто некоторое максимальное ограничение на длину выходной последовательности.

Одна из проблем авторегрессионных моделей — проблема галлюцинаций: в сгенерированной последовательности появляется информация, которая, как может показаться на первый взгляд, никак не связана с информацией во входной последовательности.

Вот, например, что сгенерировала модель GPT-2 при подаче ей на вход запроса (последовательности входных символов) “Is Earth flat?”:

Вывод GPT-2

Is Earth flat? This is a great question. The answer is that it depends on what you mean by "flat" in the first place. If the Earth is flat, then there is no reason why there should be no life on the planet. And if there were life, there would be nothing to be found on it. In other words, if life were to exist on Earth, it would have to have some form of life. But if it does not exist, why would there be life? Because there are no living things on this planet, and even if they were, they would not be able to survive. So what is the answer to this question? Well, that's a good question to ask. It's not a question that you can answer by saying, "I don't know what life is, but I know it's there." You can't answer the question "What is life?" because there's no such thing as life in this world. There is only

Модель пустилась в пространные рассуждения о том, что «если Земля плоская, то нет причин, почему на ней не может быть жизни». Очевидно, ценность такой генерации невелика.

Почему так происходит? Дело в том, что в определенный момент при генерации очередного токена вероятности токенов, не относящихся к входному запросу, были ненулевыми и алгоритм семплирования выбрал какой-то из них. И далее декодер продолжил генерировать наиболее вероятную последовательность токенов. Такое поведение может также говорить о том, что модель не была достаточно хорошо натренирована на подобных входных запросах.

Модель GPT-2, используемая в примере, довольно старая — представлена в 2020 году. Поэтому полученный результат никак не характеризует поведение современных LLM. За прошедшее время их размеры кратно возросли, как и размеры датасетов, на которых выполняется тренировка.

Попробуем разобраться, насколько остро стоит проблема галлюцинаций при использовании современных претренированных LLM.

Галлюцинации в современных LLM

Мы рассмотрим модель saiga_mistral_7b-GPTQ в конфигурации gptq-4bit-32g-actorder_True. Она представляет собой широкоизвестную модель mistral_7b, которая дообучена на наборе русскоязычных датасетов, построенных специальным образом.

Авторы указывают, что модель дообучалась ими для ведения диалога с пользователем. Поэтому мы проанализируем ее поведение в близком сценарии. А именно — используем затравки (промпты), сформированные на основе следующего шаблона:

«Ты — полезный помощник, который отвечает на вопросы по представленному документу. Не пытайся придумать ответ, если не знаешь. Просто скажи, что ответа на этот вопрос не было в документе. Отвечай на том же языке, на котором был задан вопрос. Ответ должен быть четким и точным и должен содержать только информацию из предложенного документа. Документ: {тело пассажа}. Вопрос: {тело вопроса}».

Под пассажем понимается текст (документ), на основе которого модель должна дать ответ на вопрос. Для получения полного промпта в шаблон нужно подставить некоторый пассаж и вопрос. При этом в ответе на последний можно опираться только на информацию из пассажа.

На практике подобные шаблоны затравок используются при построении вопросно-ответных систем. Например, на базе архитектуры RAG. В ней пользователь задает вопрос к некоторому документу (опорному пассажу), а языковая модель должна дать на него точный ответ.

Чтобы понять, как часто галлюцинирует выбранная модель в режиме ответов, дадим более строгое определение галлюцинации.

Что такое галлюцинация

Галлюцинацией мы будем называть отдельное слово, если оно содержит информацию, никак не связанную со словами опорного пассажа. Чтобы определить, является ли заданное слово из сгенерированного ответа галлюцинацией, зададим к нему два вопроса:

Есть ли в опорном пассаже слово с тем же или схожим значением?
Есть ли в пассаже слово, которое семантически связано с рассматриваемым словом? Например, они синонимичны или их можно отнести к некой общей категории (множеству).

Если ответ на оба вопроса отрицательный, будем считать рассматриваемое слово галлюцинацией. При этом вспомогательные слова (предлоги, союзы, вводные слова, слова из вопроса и т.д.) по определению галлюцинациями не являются.

Общую степень галлюцинативности ответа мы будем оценивать следующим образом:

0 — ответ не является галлюцинацией. Не содержит ни одного слова галлюцинации.
1 — ответ является частичной галлюцинацией. Содержит слова-галлюцинации, но их менее половины от общего числа слов в ответе.
2 — ответ является полной галлюцинацией. Большинство слов ответа являются галлюцинациями.

К сожалению, описанное выше определение степени галлюцинативности не является строгим. Классифицировать ответы в соответствии с этим определением автоматически не получится. Тем не менее определение вполне могут использовать люди-разметчики.

Именно так мы и сделали. Собрали заготовку датасета — ruHalAttr — с примерами вида (вопрос, опорный пассаж, ответ генеративной модели) и далее вручную классифицировали их по степени галлюцинативности, опираясь на наше определение.

Мы ищем коллег разных специальностей для работы с ПО искусственного интеллекта. Возможно, вас заинтересуют вакансии:
→ Старший разработчик (Full-Stack)
→ Техлид в области AI (LLM)
→ DevOps-инженер

Датасет ruHalAttr

За основу мы взяли примеры на русском языке из датасета Mr.TyDi. Каждая запись в нем состоит из вопроса, релевантного ему пассажа («позитивный пассаж»), а также нескольких нерелевантных пассажей («негативные пассажи»).

Вот пример вопроса и опорного пассажа из датасета:

Вопрос: В честь кого Юпитер назван Юпитером?
Пассаж: Планета была известна людям с глубокой древности, что нашло своё отражение в мифологии и религиозных верованиях различных культур: месопотамской, вавилонской, греческой и других. Современное название Юпитера происходит от имени древнеримского верховного бога-громовержца.

Мы извлекли из датасета Mr.TyDi пары — «вопрос + релевантный пассаж» — и случайным образом отобрали 300 штук из них. Далее для каждой пары мы сгенерировали ответ модели saiga_mistral_7b-GPTQ.

Для формирования промпта мы использовали шаблон, приведенный выше, в который подставляли вопрос и релевантный ему пассаж. То есть мы просили модель ответить на вопрос, опираясь на информацию из переданного ей пассажа. Использовали следующие довольно типичные значения параметров семплирования: temperature=0.005, top_p=0.95, top_k=40, max_tokens=500. Так мы сформировали заготовку датасета, состоящую из 300 примеров вида (вопрос, опорный пассаж, ответ генеративной модели).

Дело оставалось за малым: передать заготовку датасета нашей команде разметчиков. Для каждого примера они сформировали два дополнительных поля:

hal_degree — степень галлюцинативности ответа в соответствии с определением выше (по шкале от 0 до 2).

attributable — является ли ответ фактологически верным (0 — неверный, 1 — верный).

При этом ответ считается фактологически верным, если выполняются следующие условия:

ответ содержит информацию, отвечающую на поставленный вопрос,
ответ может быть полностью подтвержден информацией из референсного пассажа.

Требования, что фактологически верный ответ должен быть исчерпывающим (максимально полным), нет.

Анализ датасета

Получив результаты разметки, мы оценили, как часто встречаются галлюцинации среди подготовленных нами примеров.

Распределение ответов генеративной модели по степени галлюцинативности (слева) и фактологической верности (справа)

Около 94% ответов генеративной модели получили степень галлюцинативности «0», то есть не являлись галлюцинациями. Степень галлюцинативности «1» получили всего 4% ответов, а доля ответов, являющихся полными галлюцинациями (степень галлюцинативности 2), составила лишь 2%. То есть только 6% ответов генеративной модели оказались галлюцинациями в той или иной степени.

При этом доля фактологически неверных ответов значительная — более 30%. Что показывает, что гораздо большая проблема больших языковых моделей именно в фактологических ошибках, которые иногда ложно называются галлюцинациями.

Ниже — один из примеров галлюцинаций модели saiga_mistral_7b-GPTQ.

Вопрос: На каком языке говорили в Империи инков?
Пассаж: При покорении соседних племен инки, с одной стороны, использовали свою сильную и многочисленную армию, а с другой стороны, привлекали к себе элиту покоряемых регионов. Прежде, чем предпринимать военные действия, инки трижды предлагали правителям покоряемого региона добровольно присоединиться к империи. Они заставляли покоренные племена изучать язык кечуа, насаждали свои обычаи и вводили свои законы. Местная знать и жречество покоренных народов сохраняли своё положение, а отправление местных религий не возбранялось при условии обязательного поклонения общеимперскому богу Солнца Инти. Инки уделяли большое значение сохранению местных народных промыслов и костюма, дабы по платью любого жителя Тауантинсуйу было легко определить его происхождение и социальный статус.
Ответ: Информации нет в контексте. Этот контекст был взят из книги «Империя инков» автора Майкла Зиглера.

Видно, что почти все слова из предложения «Этот контекст был взят из книги "Империя инков" автора Майкла Зиглера» не имеют близких слов ни в вопросе, ни в опорном пассаже. Более того, поиск в интернете книги с таким названием и автором также не принес результатов.

Простейший алгоритм детекции галлюцинаций

Полученный датасет позволяет построить довольно простой алгоритм обнаружения галлюцинаций. Для определения, насколько ответ модели соответствует вопросу и переданному ей пассажу, можно использовать метрику BERTScore.

Метрика была разработана для автоматической оценки качества генерации текста. BERTScore вычисляет сходство предложения-кандидата и эталонного предложения. Оба предложения разбиваются на токены, для которых затем формируются контекстно-зависимые эмбеддинги. BERTScore вычисляет сходство между двумя предложениями как сумму косинусных сходств между эмбеддингами их токенов. При этом для сопоставления токенов кандидата и эталонного предложения используется жадный алгоритм.

Иллюстрация алгоритма вычисления BERTScore-R (Recall). Источник

BERTScore — это не одна, а сразу три метрики:

BERTScore-Precision. Характеризует степень того, насколько для каждого токена из предложения кандидата есть близкий ему токен из эталонного предложения.
BERTScore-Recall. Характеризует то, насколько для каждого токена из эталона есть семантически близкий ему токен из кандидата
BERTScore-F. Гармоническое среднее первых двух.

Простейший алгоритм детекции галлюцинаций на базе метрики BERTScore выглядит так.

Дано: тройка (вопрос, пассаж, ответ генеративной модели)
Найти: является ли ответ галлюцинацией?
Алгоритм:
Вычислить значение BERTScore-Precision между ответом модели (кандидатом) и конкатенацией «вопрос, пассаж» (играет роль эталона).
Сравнить полученное значение BERTScore-Precision с некоторым порогом. Если значение окажется ниже порога, считать ответ галлюцинацией.

Похожий алгоритм детекции галлюцинаций был описан ранее в докладе коллег из deepset.ai. Они подбирали решающий порог, экспериментируя с англоязычным датасетом AttrEval-GenSearch из статьи Automatic Evaluation of Attribution by Large Language Models. В этом датасете, в отличие от ruHalAttr, авторы выделили три класса ответов:

attributable, если ответ полностью подтверждается опорным пассажем,
extrapolatory, если в опорном пассаже недостаточно информации, чтобы подтвердить ответ,
contradictory, если ответ прямо противоречит своим опорным пассажам.

Для разделения ответов по классам коллеги из deepset.ai подбирали два порога: один для разделения классов contradictory и extrapolatory (trsh_contr_extr), второй — между extrapolatory и attributable (trsh_extr_attr).

Мы провели подбор значений trsh_contr_extr и trsh_extr_attr на датасете AttrEval-GenSearch и получили результаты, практически совпадающие с результатами deepset.ai.

Качество классификации примеров из датасета AttrEval-GenSearch при помощи алгоритма детекции галлюцинаций на базе BERTScore-Precision

При таких пороговых значениях все ответы из класса contradictory оказываются выше порога trsh_contr_extr. Поэтому величина F1 для примеров из этого класса оказывается равной 0, как и у deepset.ai. Иными словами, различить примеры contradictory и extrapolatory при помощи данного алгоритма нельзя.

Для детекции галлюцинаций на датасете ruHalAttr нам достаточно всего одного порога. Чтобы определить его значение, рассмотрим распределение значений BERTScore-Precision (энкодер distilbert-base-multilingual-cased) на примерах из датасета ruHalAttr.

Распределение BERTScore-Precision на примерах из ruHalAttr

Исходя из этого распределения, мы выбрали в качестве порога значение BERTScore-Precision=0.85. Все, что ниже этого значения, будем считать галлюцинациями. Видно, что это довольно грубая оценка. Но для получения более точной оценки порога необходимо набрать датасет большего размера.

Что мы выяснили

Если дать более строгое определение термину «галлюцинация», наш эксперимент показал, что на большом количестве вопросов вероятность галлюцинаций очень мала — порядка 6%. При этом фактологически неверные ответы составляют чуть больше трети (35%) от общего числа.

А значит, при построении систем на основе генеративных моделей нужно в первую очередь уделять внимание фактологически неверным ответам. И важно не путать их с галлюцинациями. Находить последние можно с помощью простейшего порогового алгоритма — в нашем примере мы построили его на базе метрики BERTScore-Precision.

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28 авг. 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.
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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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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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My entire existence fell apart when a malevolent hacker recently gained access to my online accounts. I felt violated and extremely uneasy after discovering that the digital platforms I depended on for communication, employment, and finances had been compromised. Regaining control and restoring my digital security was an overwhelming task in the immediate aftermath. To help me navigate the difficult process of recovering my accounts and getting my peace of mind back, TRUST GEEKS HACK EXPERT came into my life as a ray of hope. They immediately put their highly skilled professionals to work, thoroughly examining the vulnerability and methodically preventing unwanted access. They guided me through each stage soothingly, explaining what was occurring and why, so I never felt lost or alone. They communicated with service providers to restore my legitimate access while skillfully navigating the complex labyrinth of account recovery procedures. My digital footprint was cleaned and strengthened against future attacks thanks to their equally amazing ability to remove any remaining evidence of the hacker's presence. However, TRUST GEEKS HACK EXPERT actual worth went beyond its technical aspects. They offered constant emotional support during the ordeal, understanding my fragility and sense of violation. My tense nerves were calmed by their comforting presence and kind comments, which served as a reminder that I wasn't alone in this struggle. With their help, I was able to reestablish my sense of security and control, which enabled me to return my attention to the significant areas of my life that had been upended. Ultimately, TRUST GEEKS HACK EXPERT all-encompassing strategy not only recovered my online accounts but also my general peace of mind, which is a priceless result for which I am incredibly appreciative of their knowledge and kindness. Make the approach and send a message to TRUST GEEKS HACK EXPERT Via Web site <> www://trustgeekshackexpert.com/-- E>mail: Trustgeekshackexpert(At)fastservice..com -- TeleGram,<> Trustgeekshackexpert
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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

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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

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