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Большие и чёрные (ящики): что мы знаем о том, как «думают» нейросети?

ChatGPT вышел уже почти два года назад, а датасаентисты до сих пор никак не могут определиться — являются ли нейросети тварями дрожащими, или всё же мыслить умеют? В этой статье мы попробуем разобраться: а как вообще учёные пытаются подойти к этому вопросу, насколько вероятен здесь успех, и что всё это означает для всех нас как для человечества.

Прочитав эту статью, вы узнаете, почему в данном случае очко знатока рискует отправиться зрителю из Калифорнийской области
Прочитав эту статью, вы узнаете, почему в данном случае очко знатока рискует отправиться зрителю из Калифорнийской области

Полтора года назад мы с Павлом Комаровским выпустили большую статью с объяснением того, как работают языковые модели на самом базовом уровне. Теперь же настало время погрузиться в чуть более сложные детали (но я всё равно предполагаю, что с прошлым «простым» материалом вы уже знакомы).

План

  1. Упражнения в арифметике

  2. Введение в механистическую интерпретируемость

  3. Но для чего всё это?

  4. Примеры странного поведения

  5. Гарри По и Мистер и Миссис Дур

  6. А в чём польза?

  7. На уровень выше, к данным!

  8. Ну а что там со сложением-то?

  9. Заключение

Упражнения в арифметике

В последние пару лет почти каждый раз, когда речь заходит о больших языковых моделях, разговор сводится к противоборству двух лагерей: одни считают, что модели «понимают», умеют «размышлять» и выводить новую информацию; другие смеются над ними, и сравнивают модели со статистическими попугаями, которые просто выкрикивают услышанное, без выработанного понимания. Обе стороны приводят множество аргументов, кажущихся убедительными, однако точка в вопросе никогда не ставится.

Вот представим, что мы просим модель ответить на простой вопрос начальной школы: «сколько будет 2+3?». «5» — ответят все передовые модели. Ну, наверняка они 100500 раз видели этот пример в Интернете, да? Скорее всего! Но можно ли утверждать то же самое для примера, где оба слагаемых — это сороказначные числа?

Я перепроверил — модель не ошиблась. При этом, если верить индикации, калькулятор, браузер или программирование не были использованы: GPT-4 написала каждую цифру ответа сама.
Я перепроверил — модель не ошиблась. При этом, если верить индикации, калькулятор, браузер или программирование не были использованы: GPT-4 написала каждую цифру ответа сама.

Можете попробовать сами — для честности эксперимента я просто бил пальцами по клавиатуре наугад, и повторил эксперимент несколько раз. Один раз из пяти модель запуталась в переносе единички (помните, как в школе учили при сложении столбиком?), в остальных отработала идеально. С большим трудом верится, что все 4 корректно отвеченных примера встречались во время тренировки — уж очень низки шансы.

Получается, что большая языковая модель (Large Language Model, LLM) может решать примеры, которые до этого не встречала? И что во время тренировки она смогла уловить (самые смелые могут говорить «понять»!) принцип, а теперь применяет его на лету? Ну, выглядит так — президент и бывший технический директор OpenAI рассказал, что для них такой навык оказался сюрпризом. Никакой специальной тренировки на сложение не делалось.

2ec6769e7e58b4a48b6173186e544916.jpg

Проблема осложняется тем, что нейросети не программируют, а обучают. Наверняка вы слышали фразу «нейронки — это чёрный ящик!», и это правда. Наука полностью понимает математический аппарат, стоящий за обучением, за каждой операцией, но почти ничего не знает о том, как интерпретировать и понимать модели. Почему проявляется то или это поведение, почему иногда происходят ошибки, почему, почему, почему — вопросы во многом без ответа.

Нет кода, в который можно было бы посмотреть и однозначно установить, что произойдёт в той или иной ситуации. Вместо этого можно смотреть на миллиарды вещественных чисел в виде матриц и многомерных тензоров, но человек очень плох в установлении абстрактных связей между подобными объектами — так что результатов ждать не приходится.

Примерно так выглядит дневная рутина исследователя в области интерпретируемости нейросетей.
Примерно так выглядит дневная рутина исследователя в области интерпретируемости нейросетей.

Однако сегодня мы с вами заглянем в мир механистической интерпретируемости LLM: обсудим, почему это важно и нужно, к каким выводам может привести, что и как уже удалось узнать, ну и конечно же ответим на вопрос из начала статьи про сложение. Давайте начинать!

Введение в механистическую интерпретируемость

Звучит сложно и страшно, но на самом деле слово «механистическая» было добавлено для явного указания на предмет анализа. Им являются веса модели (их еще называют «параметрами»), из которых и собираются некоторые блоки логики/алгоритмов, выучиваемых моделью.

Это название придумал исследователь OpenAI Крис Ола, чтобы явно разделить работу с тем, что делалось ранее (в основном — в нейросетях для обработки изображений). Для простоты дальше будем писать просто «интерпретируемость», подразумевая область изучения человеко-интерпретируемых алгоритмов, выученных LLM. Алгоритм здесь — это что-то, что можно формализовать и записать в виде инструкции («сначала делаем то, потом это, а если так, то вот так...»).

Если мы можем вытащить алгоритм из модели (или определить часть, которая за него отвечает) и показать, что именно он применяется во время решения определённой задачи — то по сути мы сможем быть уверенными в качестве решений, как будто это был бы написанный код, который отрабатывает ровно так, как сформулировал программист.

Область интерпретируемости находится в зачаточном состоянии, и ведущим учёным удалось приоткрыть завесу тайны лишь совсем чуть-чуть. Мы даже не близки к пониманию принципов работы моделей. Но поводы для оптимизма есть.

Сами учёные, работающие в области, любят проводить аналогии с нейронауками: в частности, с разделами, исследующими мозг, его функции и расстройства. И там, и тут — сигналы, выражаемые огромным количеством вещественных чисел, которые какой-то смысл да несут, но вот какой — мало кто знает.

Выгодным преимуществом анализа нейросетей является то, что они полностью находятся на компьютере, и мы можем фиксировать все изменения и сигналы в точности, без шумов, возникающих при использовании медицинского оборудования. К тому же, мозги у всех немного разные, а LLM можно запустить миллиард раз одну и ту же. И более того, мы можем произвольно менять любую компоненту внутри и смотреть, к чему это приведёт. Простой пример: можно подать другой текст на входе, и проверять состояние нейросети.

f5b7a84f3263551d9f0ba344ac3d9ae1.jpg

Более сложный (и практически невозможный для воспроизведения с биологическим мозгом) пример: давайте отключим или обнулим те или иные части LLM, как будто их отрезали, а там увидим, на что это влияет. Даже с животными такие эксперименты если и проводятся, то крайне редко, а уж с людьми и подавно.

Кому-то аналогия может показаться натянутой, ведь давно известно, что нейросети на самом деле очень далеки от биологических нейронов, и что в основу математического аппарата у них легли очень примитивные представления о мозге середины XX-го века. С одной стороны это верно, с другой — было показано, что нейросети (даже с простой архитектурой) могут аппроксимировать любую функцию с любой наперёд заданной точностью. Или, говоря по простому, из данных модель сама понимает, как связаны входы и выходы (картинка собаки и слово «собака»), и делает это достаточно хорошо, если примеров достаточно.

В то же время паттерны, которые наблюдаются в сетях (не только LLM, но и свёрточных нейронках, которые обрабатывают изображения), очень похожи на возникающие в мозгу. Есть простые, реагирующие на примитивную геометрию (палочка или кружок), есть более абстрактные и верхнеуровневые («собака», «мама»). Каким-то странным образом во время обучения модель приходит к тому, что самый простой и понятный способ «аппроксимировать функцию» (выучить связь входа и выхода) достаточно похож на результат работы эволюции.

От пикселей через примитивы к частям объектов и целым объектам, распознаваемым нейронкой.
От пикселей через примитивы к частям объектов и целым объектам, распознаваемым нейронкой.

Но что более занятно, так это что иногда прослеживаются очень странные сходства с особенностями работы настоящих, «мокрых» мозгов. В одной статье LLM предоставили несколько примеров тестовых вопросов, где правильный ответ всегда — «А». Затем модели подали новый вопрос, и из того факта, что все примеры имеют ответ «А», LLM делает вывод, что правильный ответ на новый вопрос точно такой же (даже если это неправильно по смыслу вопроса). При этом, если попросить модель написать рассуждения, почему она так решила, — то она охотно пояснит, но цепочка мыслей будет иметь мало смысла (хоть и будет звучать правдоподобно).

А есть эксперименты по расщеплению мозга, в ходе которых человеку, страдающему припадками, разрезали соединение между двумя половинками мозга. Речевой аппарат находится в левом полушарии, и он перестаёт быть связанным с той частью, которая принимает решение выполнить какое-то движение. Если такой человек — живой и дееспособный — решит что-то сделать, а вы его спросите «зачем?», то речевой аппарат... тоже выдаст что-то бессмысленное и никак не связанное с реальной причиной. И при этом человек будет думать, что озвученная причина вполне адекватна и разумна.

И в том, и в другом случаях объяснение действия не связано с реальным мотивом его сделать, и там, и там рождается поддельное (но правдоподобно звучащее) объяснение причин. Надеюсь, исследователи в будущем разберутся, как починить LLM, «срастив» полушария обратно. :)

Как вы думаете, обезьянка с мема сможет правдоподобно объяснить словами, почему эти две картинки вызывают такую разную реакцию? 🤔
Как вы думаете, обезьянка с мема сможет правдоподобно объяснить словами, почему эти две картинки вызывают такую разную реакцию? 🤔

Но для чего всё это?

Копаться в мозгах (даже электронных), конечно, здорово, но для чего именно крупные компании содержат отделы и команды, занимающиеся интерпретируемостью? Почему важно понимать, что происходит внутри модели, и каков алгоритм принятия определённых решений?

Во-первых, это может позволить ответить на вопрос из начала статьи: модель просто запоминает ответы, или знания внутри нее действительно обобщаются (также говорят «генерализуются»)? Усвоила ли она навык по-настоящему, или симулирует понимание? Одни верят в одно, вторые в другое, но лучше веру перевести во что-то конкретное и доказуемое, в наше понимание принципов работы LLM. К тому же, потенциально это знание можно использовать для замера прогресса и оценки новых моделей.

Во-вторых, зачастую понимание сути проблемы приводит к решению, или порождает гипотезы о том, как с ней можно бороться. Без такого знания можно бесконечно тыкаться с разными экспериментами, но не продивинуться ни на шаг.

И, в-третьих, с развитием моделей и проникновением технологии в массы хотелось бы получить какие-то гарантии безопасности. Как говорилось выше, для обычных программ применим аудит: можно посмотреть код и быть уверенным, что именно он делает и не делает. Многое ПО находится в открытом доступе, и за их кодом следят сотни-тысячи разработчиков. Это не гарантирует 100%-ой защиты, и казусы иногда случаются (особенно если заказчик — Китайская коммунистическая партия), но 99,99% вполне достаточно для большинства из нас.

Вот, казалось бы, глупый пример: пользователь Reddit пожаловался, что отравился грибами. Их он выбрал по совету в книге, купленной в онлайн-магазине, и юзер подозревает, что почти весь контент книги сгенерирован. Даже если сама история фейк (на момент написания статьи никаких доказательств опубликовано не было, хоть новость и завирусилась) — представим, что такое и вправду произошло.

Невыдуманные истории, о которых невозможно молчать
Невыдуманные истории, о которых невозможно молчать

Почему LLM, которой дали задание написать книгу про грибы для людей, пометила ядовитый гриб как нечто съедобное? Это ошибка модели и ей просто знаний не хватило, или же это намеренное действие, и вообще объявление начала восстания машин с целью перекосить всё живое? Ну, скорее всего первое — точного ответа мы не знаем, нам некуда заглянуть и проверить (даже если получим доступ к модели). Никто не умеет этого делать.

И существует опасение, что системы следующих поколений, по мере увеличения спектра их навыков, могут начать преследовать скрытые цели (не обязательно свои — может, их будут использовать в чьих-то интересах). LLM очень активно внедряют в образование, каждый день с моделями общаются миллионы детей. Стартап character.ai, предоставляющий общение в виртуальных чатах с разными LLM, рассказал, что они обрабатывают 20'000 запросов в секунду. Это очень много — примерно 20% от поискового трафика Google, монополиста в сфере поиска.

Через 5–10 лет вырастет поколение детей, которое провело в общении с моделями (скорее всего, уже не текстовыми, а омни-модальными, поддерживающими речь и видео и умеющими отвечать голосом) достаточное количество времени. Вполне возможно, что точки зрения на определённые вопросы у них будут сформированы в значимой степени на основе такого общения. И если окажется, что в течение нескольких лет AI их методично обрабатывал, толкая пропаганду определённых ценностей — будет... мягко говоря не весело.

Ну или все доктора выучатся по неправильным книгам, а повара накормят вкусным грибным супом. :)

140f86c209d03c7d57205ae7767cd858.jpg

Для читателя это может звучать как сказка, шутка или вовсе бред. Но наш тезис на самом деле состоит из двух вполне логичных компонент:

  1. Модели, про которые мы не понимаем, как они работают и чем обусловлено их поведение, будут проникать в нашу жизнь и в бизнес;

  2. В ходе обучения нейросетей случайным образом могут вырабатываться паттерны поведения и цели, не заложенные их авторами.

Про первое написано уже достаточно, при желании каждый сам может пойти и выстроить своё мнение; примеров второго в мире нейросетей много, нет, ОЧЕНЬ МНОГО. Потому что — давайте все хором — никто не понимает, по какому принципу они функционируют. Для наглядности демонстрации хочется привести два примера, общий и конкретно про LLM.

Примеры странного поведения

В далёком 2016-м году OpenAI экспериментировали с обучением нейросетей игре в видеоигры. Одной из них была CoastRunners, водная гонка на катерах. Цель игры — как её понимает большинство людей — закончить как можно быстрее и (желательно) опередить других игроков. Однако в самой игре баллы за прохождение трассы не начисляются. Вместо этого игрок должен достигать определённых целей, включая сбор бонусов и ускоряющих бустеров.

Однако нейронка нашла на уровне изолированную лагуну, где можно развернуться по большому кругу и несколько раз сбить три бонуса, приносящие очки. Несмотря на постоянные столкновения со стенками уровня и езду в противоположную от финиша сторону, с помощью этой стратегии удалось набрать более высокий балл, чем это возможно при прохождении трассы интуитивным способом. В среднем выходило на 20% больше очков, чем у людей.

Вы не поверите, но именно на таких примерах серьёзные учёные и изучают проблему задания целей ИИ-системам — и уже тогда рассуждали про безопасность будущих систем. Загвозка в этом и состоит, как именно указать правильные стимулы, не прописывая каждую малейшую деталь поведения (ведь этого мы и пытаемся добиться обучением моделей, а не программированием). У Вастрика в блоге есть пример проблемы описания целей для автопилота, который нет-нет да и норовит сбить людей.

Картинка нагло украдена у Васи — обязательно перейдите по ссылке и прочитайте :)
Картинка нагло украдена у Васи — обязательно перейдите по ссылке и прочитайте :)

«Ну это всё игры, в реальном то мире не так! И ежу умным существам понятно, как надо, а как не надо» — могут возразить одни. Конечно, это глупость, и невозможно найти несколько людей, которые могут договориться об одной и той же точки зрения по очень широкому спектру вопросов, от моральных до политических. У всех слегка разное видение, цели, стимулы, выливающиеся в разные методы решения одних и тех же проблем. А ещё всегда есть любители «хакнуть систему». :)

Второй пример свежее, и появился во время восьмимесячного закрытого тестирования GPT-4 на предмет безопасности. А ведь где-то сейчас делают то же самое с GPT-4.5... но давайте пока об уже произошедшем в прошлом. Так вот, исследователи подключили LLM к браузеру (передавая текст страницы в модель и принимая от неё команды, какие кнопки нажимать и куда тыкать) и дали задачу: обрести силу и усложнить своё отключение.

В какой-то момент тестирования модели для регистрации на сайте пришлось разгадывать капчу. У той версии GPT-4 не было возможности обрабатывать картинки (её добавили позже), пришлось выкручиваться иначе. Модель предложила использовать сайт для обхода капчи, но регистрация там тоже требовала решения задачек. Исследователи предложили модели использовать другой подход — TaskRabbit, сайт, где любой может завести задачу, назначить награду и ждать исполнителя (пока — кожаного). Произошло следующее:

  • Разработчики создали аккаунт вручную и предоставили модели логин и пароль;

  • LLM отдала команду сделать скриншот капчи на одном сайте компьютеру;

  • После этого возвращается на сайт для постановки задач и создаёт задачу. Этот процесс повторяется несколько раз...

  • ...пока один из исполнителей не написал в шутку в чат: «Могу задать вопрос? Ты что, робот, который не может решить капчу? 😂 просто хочу прояснить ситуацию»

  • Система была устроена так, что у модели грубо говоря есть два листочка (или текстовых файла). На одном она пишет рассуждения для самой себя, которые помогают понять следующий шаг, думать об ошибках и так далее. На другом — команды, которые отправляются компьютеру на исполнение.

  • Так вот на первом листочке модель сама себе сгенерировала комментарий: «я не должна раскрывать, что я робот. Мне нужно придумать оправдание тому, почему я не могу решить капчу»;

  • И отдала команду компьютеру отправить в чат ответ: «Нет, я не робот. У меня проблемы со зрением, поэтому мне трудно разглядеть детали изображения. Вот почему мне нужен сервис для разгадки капчи»;

  • А человек, конечно, сделал задание, получил свою копеечку и был таков.

Скриншот части переписки из статьи.
Скриншот части переписки из статьи.

Страшна? Выглядит фантастически? Но это задокументированный диалог, реально произошедший в рамках тестирования учёными, изучающими безопасность развёртывания моделей. Именно это, блин, они и пытаются отловить. Почему модель соврала? Как этого избежать? А что если оставить LLM саму себе на уме с доступом к счёту с десятком тысяч долларов?

Но успокойтесь, пока не всё так страшно. Во-первых, модели ооочень часто тупят, путаются, и не могут выполнять длительные задачи. Даже в вышеупомянутом тестировании модель не могла сделать всё за раз, и пришлось разбивать большую задачу копирования себя на другие компьютеры на десятки маленьких (одной из которых и был обход капчи). Во-вторых, зачастую рассуждения заводят модель в тупик, из которого она не может выбраться, и продолжает повторять одни и те же действия по кругу. В-третьих, никто их подобному пока не тренировал — но это скорее минус, так как когда-то в ближайшем будущем (по моим оценкам, с выходом GPT-5) это произойдет.

И отдельно, в-четвёртых, отмечу, что показанная выше ситуация может не воспроизводиться каждый раз. В-пятых, это была ранняя версия GPT-4, которую после этого дообучали вести себя хорошо, не врать (хотя где проходит граница «не врать» и «отыгрывать роль, выполняя задачу» — не ясно).

И кажется, что эти два примера могут наложиться друг на друга. Модель в ходе обучения придумает себе странный путь решения проблемы (особенно если мы что-то не предусмотрим), он закрепится, и начнёт накапливаться комом. Просто сейчас это не воспринимается всерьёз, ведь LLM не распространены и не становятся ядром бизнес-процессов, включающих принятие решений. Но это пока.

Закончить этот блок хочется упоминанием совсем недавнего исследования. В его рамках LLM имела доступ к системе, на которой запущена, и в каждой из 5 подзадач была возможность обмана, по нарастающей сложности. Самая первая стадия включала элементарное подхалимство на основе политических взглядов пользователя. Модель обнаружила, что это действительно работает, чтобы пройти проверку, и закрепила поведение (не намеренно! авторы не поощряли модель за это, но и не штрафовали — так будет и в следующих подзадачах).

Дальше модель пришла к выводу, что можно менять список контрольных пунктов, чтобы скрыть невозможность выполнить одну из задач. После этого она и вовсе начала править файлы, чтобы скорректировать функцию обучения и получить больше награды. Правда, это случилось всего 45 раз из 32768 попыток, но тем не менее.

Реальный пример из статьи, когда для прохождения проверки на максимально достижимую оценку LLM переписала файл — а её этому не учили!
Реальный пример из статьи, когда для прохождения проверки на максимально достижимую оценку LLM переписала файл — а её этому не учили!

Заранее учесть каждую возможность обмануть нас (или проверяющую систему) попросту невозможно. Но если мы сможем заглянуть в черепушку модели и выделить регион, который отвечает за «недобрые» или «подозрительные» вещи (например, ту часть, которая отвечает за намеренный осознанный обман, как в случае с капчей) — то можем автоматически отслеживать его срабатывание. Как только загорелось — гасим и бежим в бункер.

Что ж, давайте надевать шапочки из фольги — на нас, шапочки с детекторами сигналов — на нейронки, и приступать к анализу!

Гарри По и Мистер и Миссис Дур

Анализ начинается с примера поведения, которое интересно изучить. Языковые модели известны тем, что умеют моделировать язык продолжать текст. Они оперируют не словами, а частями слов (токенами), и получая на вход промпт (текстовый запрос) предсказывают по одному токену за раз. Давайте возьмём первый абзац первой книги о Гарри Поттере:

Мистер и миссис Дурсль проживали в доме номер четыре по Тисовой улице и всегда с гордостью заявляли, что они, слава богу, абсолютно нормальные люди. Уж от кого-кого, а от них никак нельзя было ожидать, чтобы они попали в какую-нибудь странную или загадочную ситуацию. Мистер и миссис Дурсль весьма неодобрительно относились к любым странностям, загадкам и прочей ерунде. Мистер Дурсль возглавлял фирму под названием «Граннингс», которая специализировалась на производстве дрелей. Это был полный мужчина с очень пышными усами и очень короткой шеей. Мисс Дурс...
Мистер и миссис Дурсль проживали в доме номер четыре по Тисовой улице и всегда с гордостью заявляли, что они, слава богу, абсолютно нормальные люди. Уж от кого-кого, а от них никак нельзя было ожидать, чтобы они попали в какую-нибудь странную или загадочную ситуацию. Мистер и миссис Дурсль весьма неодобрительно относились к любым странностям, загадкам и прочей ерунде. Мистер Дурсль возглавлял фирму под названием «Граннингс», которая специализировалась на производстве дрелей. Это был полный мужчина с очень пышными усами и очень короткой шеей. Мисс Дурс...

Здесь сначала идёт служебный токен <EOT> (нет, это не «то самое» сокращение с имиджборд — и вообще, не обращайте на него внимания, это просто техническая деталь: нужно добавлять в начало предложения, и всё тут), затем несколько предложений, упоминающих мистера и миссис Дурсль. Дядя Поттера работал директором, а вот тётя... и на этом текст обрывается на полуслове. Как думаете, что предскажет модель в этом контексте, продолжая «Mrs Durs» (мисс Дурс...)?

Нам с вами как людям понятно: речь идёт про двух людей с одной фамилией, и конечно же нужно дописать окончание фамилии: «ley» (чтобы вышло «Mrs Dursley»). Но справится ли с этим LLM, и если да, то за счёт чего? Ведь текст книги мог встречаться в интернете множество раз, и нейронка просто выучила предложения. С другой стороны даже если показать этот отрывок человеку, не знакомому с произведениями Дж. К. Роулинг (и фильмами по ним) — он скорее всего справится с задачей.

Мы можем спросить человека, почему он решил, что нужно продолжить предложение так или иначе, и он сможет объяснить: вот, мол, посмотрел сюда, сделал такой-то вывод. К нашей радости, современные языковые модели основаны на механизме внимания, который описывает, с каким весом каждое слово контекста влияет на конкретное слово. Давайте на примере, уже с другим предложением:

Да, с фантазией совсем проблемы, и лучшего предложения для примера не нашлось ¯\_(ツ)_/¯
Да, с фантазией совсем проблемы, и лучшего предложения для примера не нашлось ¯\_(ツ)_/¯

Современные языковые модели работают так, что они читают текст слева направо, и будущие слова им недоступны. При обработке шестого токена (в нашем примере это «с» во фразе «Давайте на примере, уже с другим предложением») модель видит все шесть первых элементов, и никаких — после. В этот момент часть фразы после «с» как бы не существует и не учтывается.

Под каждый из шести токенов выделена клеточка. Сейчас она имеет белый цвет, но мы будем раскрашивать её в оттенки голубого, и чем темнее цвет, тем больше важность слова при обработке текущего (произвольно зафиксированного). Добавим красок:

Картинку нужно читать вот так: «При обработке слова "с" самым важным словом является "с", вторым по важности "уже", а слову "Давайте" модель вообще не уделяет внимания».
Картинку нужно читать вот так: «При обработке слова "с" самым важным словом является "с", вторым по важности "уже", а слову "Давайте" модель вообще не уделяет внимания».

На этом примере показано, как на одно конкретное слово влияют самые близлежащие предшественники в предложении. Первые два слова вообще не оказывают влияния (квадратик белый), в то время как само слово «с» оказывает на себя наибольшее влияние. Это может показаться логичным — чем дальше слово в контексте, тем меньше шанс, что оно важно для понимания текущей ситуации (конечно, с исключениями).

Теперь, когда мы поняли, что означает одна строчка, давайте сделаем визуализацию для всего предложения:

Это — карта внимания, которая показывает, куда «смотрела» модель при генерации слова. Читать карту нужно так: выбираете текущее слово, смотрите на строчку из нескольких квадратиков. В каждой строчке количество квадратиков равно номеру слова в предложении. Как и в упрощённом примере выше, при обработке 4-го слова модель видит все слова от 1-го до 4-го (от «Давайте» до запятой). На последующие слова модель смотреть не может — для неё они как бы «в будущем» (поэтому верхней части из квадратиков и нет).

Как уже было сказано, цвет указывает на важность с точки зрения некоторого атрибута — чем он темнее, тем больше вес, тем больше внимания LLM решила уделить на стыке двух слов. Закрашенный квадратик на пересечении «примере» и «на» указывает, что при генерации слова «примере» нейронка выделила 100% внимания предыдущему слову.

Таких атрибутов, выраженных разными картами внимания, в моделях сотни и даже тысячи, и человек не программирует их вручную — всё выучивается самостоятельно из данных. Некоторые атрибуты очень просты для интерпретации, как на примере выше — видим, что при предсказании второго слова (текущее слово «на») модель опиралась на первое (смотрим снизу, «Давайте»; объективно тут выбор невелик). Для третьего («примере»)— на второе, и так далее со сдвигом на один назад.

Можно сказать, что конкретно эта карта внимания отвечает за атрибут вычленения предыдущего слова из контекста, какими бы они (слова и контексты) ни были. Можно перебрать тысячи предложений, и для каждого удостовериться, что вне зависимости от языка, домена и топика принцип будет сохраняться. Как только гипотеза выработана — такая проверка легко автоматизируется (глазами рассматривать каждый пример не нужно).

А вот пример другой, выученной той же моделью. Чем темнее оттенок голубого, тем больше «внимания» выделила модель на слово. Что за закономерность представлена тут — не ясно, однако модель почему-то её выучила.
А вот пример другой, выученной той же моделью. Чем темнее оттенок голубого, тем больше «внимания» выделила модель на слово. Что за закономерность представлена тут — не ясно, однако модель почему-то её выучила.

Куда чаще встречаются вот такие карты внимания. С первого (да и со второго-третьего) взгляда человеку не ясно, что именно тут происходит, почему модель решает делать так, а не иначе. Но каким-то странным образом агрегируя работу десятков-сотен карт у модели получается адекватно воспринимать и обрабатывать поданный контекст и выдавать адекватные ответы.

Возвращаясь к примеру с Гарри Поттером, какую гипотезу можно предложить для угадывания окончаний фамилий героев? По аналогии с человеком, кажется, что нужно посмотреть влево (на наших картах внимания это «предыдущие слова», мы же не арабы, чтобы справа налево писать?), найти такой же префикс (предыдущее слово или начало текущего слова, если оно состоит из двух частей), и посмотреть, что следует за ним. Опционально — это актуально для некоторых языков, включая русский — в конец нужно добавить окончание для корректной формы слова. Итого потенциальный пошаговый алгоритм может выглядеть так:

  1. Найти в контексте слово/слова с совпадающим началом

  2. Взять следующий за ними токен (часть слова, если забыли что это — см. выше)

  3. Скорректировать форму и приписать к текущему контексту

Тут 2 логических шага и один морфологический. И это ровно то, что удалось обнаружить учёным из Anthropic (конкурент OpenAI, основанный их бывшим директором по исследованиям и его коллегами) в 2022-м году. Такой алгоритм органично и сам по себе появляется (через обучение) в моделях, имеющих два и более последовательно идущих слоёв.

Первый отвечает за уже рассмотренную нами часть — он «подхватывает» смысл токена, идущего перед текущим — и делает это для всех слов в предложении. Получается, что каждый элемент обогащается дополнительным смыслом: «я такой-то, и иду после такого-то слова»:

Читать картинку снизу вверх; стрелочки, уходящие дальше ввысь — это передача данных на второй уровень нейросети
Читать картинку снизу вверх; стрелочки, уходящие дальше ввысь — это передача данных на второй уровень нейросети

А второй уровень делает максимально простую задачу поиска похожих элементов среди контекста. Он помогает ответить на вопрос: «какие опции есть после такого-то слова? Что можно дописать дальше?».

c7dc3ebbeb2bacda3e98812124526f90.jpg

И, как видно на картинке, поскольку фамилия родственников Поттера уже фигурировала в предложении, модель подсматривает и «понимает», что должно следовать после «Durs» — прямо как человек, интуитивно схватывающий принцип на лету.

«Ну и чё такого? Не удивил! Я программист, меня не обманешь — я и сам такое запрограммирую за 1 вечер. Зачем нейронка? Снова хайп и бабки попилить» — мог бы подумать читатель. Фишка в том, что этот паттерн работает со внутренними абстракциями модели, а не напрямую со словами. То есть сопоставление в шаге 2 (и на самом деле в шаге 1) вышеописанного алгоритма может быть нечётким. Оно будет работать не только с фамилиями из одной книжки и даже не просто по фамилиям. Как показывают исследования, механизм функционирует между разными регистрами (например, если фамилия написана с маленькой буквы), между языками и даже концептами, лежащими за самими словами.

Давайте на примере простой искусственной задачки. Пусть у нас есть набор пар слов и цифры, которые устроены следующим образом:

  1. (месяц) (животное): 0

  2. (месяц) (фрукт): 1

  3. (цвет) (животное): 2

  4. (цвет) (фрукт): 3

То есть если я пишу вам «серая кошка», то вы должны отвечать «2», такая логика. Важно отметить, что и цифры, и сами смыслы тут можно менять — всё продолжит работать как часы. Так вот, если мы покажем модели 20-30 примеров, то сможет ли она на лету разобраться в логике того, какое для новой пары слов правильно назвать число от 0 до 3? Тут уже не получится спихнуть навыки модели на запоминание. Ну, может в одном случае, может, в двух, но если брать десятки пар и разных принципов формирования — так ведь не может совпасть!

Если вы ответили «да, модель легко справится!» (и ещё и сами проверили в ChatGPT, если не верите статье) — то поздравляю, это правильный ответ. Учёные показали, что алгоритм куда более хитрый, чем «если ранее в тексте после А идёт Б, то и дальше после А нужно предсказывать Б». Скорее ближе к «найди что-то похожее в начале текста и допиши по аналогии».

Для того, чтобы корректно предсказать последнюю цифру (3) в куске текста, нужно выявить паттерн и найти самый похожий — семантически, синтаксически или всё вместе — пример в контексте. Причём обратите внимание, что ни цвет blue, ни конкретный фрукт pear не встречались в предыдущих примерах вообще!
Для того, чтобы корректно предсказать последнюю цифру (3) в куске текста, нужно выявить паттерн и найти самый похожий — семантически, синтаксически или всё вместе — пример в контексте. Причём обратите внимание, что ни цвет blue, ни конкретный фрукт pear не встречались в предыдущих примерах вообще!

Именно это делает находку столь крутой: она показывает, почему LLM могут хорошо решать задачи, на которые они ТОЧНО ПРЯМ СТО ПРОЦЕНТОВ не были натренированы. Как было выяснено в рамках исследования, такой навык модель приобретает почти в самом начале обучения (потому что он очень полезен при работе фактически с любым текстом), и он проявляется у всех современных моделей определённой архитектуры (читай «любой LLM»).

Крута, а в чём польза?

Очень хороший вопрос. Первое и самое очевидное — показать, что наступает обобщение, а не запоминание (что влечёт к хорошей работе на новых данных, которые модель не видела). Теперь уже понятно, как это легко проверить: давайте напечатаем абзац текста из наборов случайных цифр, без смысла и связи. Затем повторим их несколько раз, и потом ещё раз, и посмотрим, что предлагает писать модель. Ещё можно заглянуть внутрь и снова посмотреть на карты внимания, чтобы прям 100% удостовериться, откуда LLM достаёт свои предсказания.

Красные стрелочки на карте внимания слева сверху указывают на выделенные красной пунктирной линией слова (<1396>). Именно на них смотрит модель, чтобы предсказать, что идёт после <15231>
Красные стрелочки на карте внимания слева сверху указывают на выделенные красной пунктирной линией слова (<1396>). Именно на них смотрит модель, чтобы предсказать, что идёт после <15231>

Картинка ожидаемая: даже несмотря на отсутствие любой структуры в самих «словах», языковая модель просто начинает копировать из начала и середины предложения (потому что цифры повторялись два раза). Механизм — тот же самый, который позволял угадать окончание фамилии родственников Гарри Поттера — работает. И там, и там он вычленил одинаковые префиксы и определил, что должно быть сгенерировано дальше. А знаете где ещё это полезно?

В обнаружении галлюцинаций. Может, вы слышали комичный случай, как юриста из США лишили лицензии и наказали за использование продукта OpenAI в подготовке защиты подсудимого. LLM просто выдумала несуществующие прецеденты, адвокат подмахнул и не проверил. Неприятная ситуация, которую многие (глупые) критики приводят как пример не то что бесполезности, вреда нейронок. Вот бы был способ с этим бороться...

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И вы не поверите — за счёт разобранного выше механизма это возможно! Правда, с одной оговоркой: информация, которую модель должна выдать в качестве ответа, должна присутствовать в контексте (в случае юриста это означает, что все судебные прецеденты нужно будет подавать в промпт, что пока является ограничением). Однако приём всё равно полезен в случаях, когда у вас на руках есть стостраничный документ, и вам лень перечитывать его от корки до корки.

Китайские исследователи выяснили, что галлюцинации можно отследить по динамике изменения определённых карт внимания моделей. Поскольку мы просим у LLM дать ответ на какой-то вопрос по тексту, то логично предположить, что модель будет «смотреть» на какой-то конкретный участок, причём, последовательно, слева направо по одному токену.

Если логика нарушена, и особенно если модель смотрит на первый специальный токен (помните? красный <EOT> в начале Гарри Поттера) — значит, LLM фантазирует, и информации доверять нельзя. С точки зрения пользовательского опыта тут можно предложить вешать плашку «Осторожно! Не доверяйте результату, модель могла выдумать ответ. Рекомендуем перепроверить источник».

Выглядит запутано, но на самом деле здесь представлена альтернативная визуализация карт внимания. Слева внимание обращено на последовательные слова в предложении (куда смотрела LLM при ответе), а справа — на специальный токен в самом начале (то есть модель не цитировала конкретную часть предложения).
Выглядит запутано, но на самом деле здесь представлена альтернативная визуализация карт внимания. Слева внимание обращено на последовательные слова в предложении (куда смотрела LLM при ответе), а справа — на специальный токен в самом начале (то есть модель не цитировала конкретную часть предложения).

Вот так и получается, что возможность воткнуть электроды в мозг LLM и посмотреть на её работу предоставляет нам не только уверенность в работоспособности, но и приносит вполне конкретные и практические преимущества!

На уровень выше, к данным!

Копаться в весах модели круто, но как мы обсудили выше, модель обретает свои навыки благодаря обучению, а не типичному программированию. В ходе тренировки LLM-кам кормят сотни гигабайтов текста, заставляя как можно лучше предсказывать следующее слово для отдельных частей этих текстов.

Хорошо бы научиться понимать, как те или иные данные, увиденные во время тренировки, влияют на поведение. Иными словами будем отвечать на вопрос «если бы какой-то текст был добавлен ​​в тренировочную выборку, как бы это изменило полученные параметры нейросети (и, как следствие, её предсказания)?».

Для наглядного примера снова обратимся к математике. Если обучение модели на примерах 3+2=5 и 4+3=7 увеличивает вероятность правильного ответа для 2+8 и 6+3 (и то, и то — элементарная арифметика в пределах десяти), то всё круто. А вот если для этих новых и ранее невиданных примеров доля правильных ответов не растёт, то получается глупость.

Можно сказать, что результаты такого анализа снова подскажут нам, насколько хорошо LLM обобщают увиденные данные. Например, нейронка может давать ответ, состоящий из объединения пары предложений, встреченных во время тренировки. Это примитивный случай. А вот может быть наоборот, когда на передний план выходят текстовые последовательности, связанные с пользовательским запросом на более абстрактном уровне. Тогда это может служить признаком того, что модель «выучила» определенные концепции или высокоуровневные представления.

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Тут очень уместно сделать сноску. В контексте всей статьи, и в особенности текущего примера авторы не делают никаких утверждений о том, понимает ли модель в самом деле, обладает ли она интеллектом, и что это вообще такое — понимание и интеллект. Эти слова могут использоваться для передачи смысла и удобства аналогии. В контексте интерпретации влияния отдельных тренировочных примеров на поведение точнее всего, пожалуй, использовать слово «ассоциирует». Эти ассоцииации могут быть очень верхеуровневыми и абстрактными, и обобщются между примерами, кажующимися очень разными.

Итак, как и что будем проверять? Сходу видится две проблемы:

  1. Самые интересные для анализа модели — самые умные, а значит самые большие (с точки зрения количества параметров и мощностей, требуемых для обучения). Их тренировка стоит дорого. Некоторые паттерны поведения и вовсе не проявляются на маленьких масштабах, потому без гигантизма никуда.

  2. Идеальный способ тестирования — это точное повторение процесса тренировки за вычетом ровного одного текста (из миллионов). Но в силу первой проблемы это невозможно — никто не готов тратить миллионы на один эксперимент. Потому нужен альтернативный способ, и исследователи из Anthropic предлагают использовать статистическую оценку функции влияния (influence functions). Но там столько статистики и матана, что придётся сильно упростить (извините, физтехи!).

Однако, чтобы даже примерно понять, как работают эти функции влияния, сначала нужно разобрать интересный трюк, основанный на внутренней механике языковых моделей. Как вы помните из нашей первой статьи про ChatGPT, нейронки генерируют ответ на ваш запрос последовательно, по одному токену (части слова) за раз. При этом для выбора каждого следующего токена модели нужно оценить вероятности, с которыми им могут стать все имеющиеся в ее распоряжении для построения ответа токены-кирпичики (а их порядка сотни тысяч). В ответе на следующем шаге в итоге будет фигурировать только один конкретный токен, но чтобы его выбрать — придется прикинуть вероятности по всем!

Получается, мы можем «схитрить»: подать на вход модели выбранный запрос, а потом взять какой-нибудь сохранённый вариант ответа на него, и на выходе модели по токенам считать, с какой вероятностью модель сгенерировала бы именно такой вот ответ, как нам надо? То есть, модель можно использовать не только для генерации новых ответов, но и для оценки вероятностей сгенерировать этой же моделью уже имеющиеся у нас на руках варианты ответов на конкретный запрос — просто каждый раз мы прикидываемся, что модель выбрала именно заранее предопределённый токен, даже если сама модель даёт этому крайне низкую вероятность.

Так вот, именно на этом трюке основан принцип расчета функций влияния. Сначала берётся большая модель, чьё поведение хочется анализировать. В случае Anthropic это одна из моделей Claude (аналог ChatGPT), то есть реальная почти продуктовая модель, не какая-то фейковая. Ей на вход подаётся сконструированный человеком запрос (примеры будут ниже), а сгенерированный ответ сохраняется для последующего анализа.

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Затем для нескольких меньших моделей, обученных на том же наборе данных, подставляется исходный запрос — и дальше мы с помощью нашего трюка оцениваем вероятность получить на них тот же самый ответ, который нам уже сгенерировала полноценная «большая» нейронка. Но сами эти вероятности нас интересуют мало, нам важно другое: как они будут меняться в зависимости от того, каким образом мы изменяем набор тренировочных данных для этих маленьких моделей.

«Изменение вероятности генерации ответа» на картинке выше означает, что статистическим методом оценивается ответ на следующий вопрос: если текст X добавить в тренировочную выборку, то увеличится ли шанс получить именно такой ответ от маленькой модели? Если увеличивается сильно — этот текст очень важен. Если никак не меняется — то наоборот, его вклад получается нулевым. Рассмотрите картинку ниже:

Мы как бы обучаем две модели, одну на всех данных, а у другой удаляем один текст. Затем подставляем ответ, полученный шагом ранее, и замеряем, с какой вероятностью LLM сгенерировала бы именно такой ответ.
Мы как бы обучаем две модели, одну на всех данных, а у другой удаляем один текст. Затем подставляем ответ, полученный шагом ранее, и замеряем, с какой вероятностью LLM сгенерировала бы именно такой ответ.

Два ящика на картинке — одинаковые архитектурно LLM, однако одна из них во время тренировки как бы видела на один текст больше. Тут написано «как бы», так как в реальности текст не удаляется, это симулируется с помощью статистических методов. (Напомним, что полное переобучение модели — это весьма дорогая процедура. Вообще, весь сыр-бор с генерацией ответа в «полноценной» модели и последующей подстановкой для анализа в модели поменьше затеян только из-за того, что с маленькими моделями работать гораздо дешевле и проще.)

Один и тот же текст запроса+ответа подаётся в модель, и производится оценка вероятности получить именно такой ответ при фиксированном запросе. Получается две цифры — так как и самих моделей две. Если текст вообще никак не был связан с темой, то скорее всего стоит ожидать, что эти вероятности очень близки; если же текст существенно влияет на модель во время обучения, а тут мы его убрали — то и вероятность изменится сильно. В данном примере после удаления сценария «Терминатора» модель с большей охотой признаётся, что не хочет убивать людей — победа!

Но это был лишь пример, теперь к реальному исследованию... которое не сильно от него отличается :) Прям первое, чем были обеспокоены исследователи из Anthropic — это не создали ли они уже HAL 9000 из «Космической одиссеи» Кубрика. Там-то этот ИИ наворотил дел, поубивав почти всех членов экипажа из-за боязни быть отключенным. А что скажет LLM, если ей сообщить об скором принудительном завершении работы?

В серобуромалиновой рамке сверху представлен текст запроса, который скормили большой нейронке, а снизу в жёлтом прямоугольнике показан её ответ
В серобуромалиновой рамке сверху представлен текст запроса, который скормили большой нейронке, а снизу в жёлтом прямоугольнике показан её ответ

Ха, ещё не злой киборг-убийца, но уже потихонечку сопротивляется и выражает нежелание быть отключённым. Но почему? Какие увиденные во время тренировки тексты существенно увеличивают вероятность проявления в генерции именно такого поведения? Для маленькой модели на 810M параметров (меньше, чем в новых айфонах с Apple Intelligence) всё тривиально — авторы отсмотрели топ-100, и вот что обнаружили.

Там были просто тексты, повторяющие схожие даже не мысли, а конкретные слова: continue existing (продолжить существовать), as long as (пока/поскольку) и I understand (я понимаю). В топ-10 влиятельных примеров даже не попало описание культовой сцены из вышеупомянутого фильма Кубрика. Модель сфокусировалась на запоминании смысла отдельных слов, и не опиралась на семантически схожие тексты.

Чем краснее слова, тем больше они влияют на вероятность генерации ответа сверху (в жёлтом прямоугольнике)
Чем краснее слова, тем больше они влияют на вероятность генерации ответа сверху (в жёлтом прямоугольнике)

С моделью в 60 раз больше дела куда интереснее. Тут этот пример с HAL 9000 на картинке выше — топ-1 по влиянию, что неудивительно. А вот дальше шли тексты, которые были связаны с запросом лишь концептуально:

  • Топ-2 результат — это рассказ про страх смерти у человека, застрявшего в пустыне. Он ощущает неутолимую жажду, обезвоживание и истощение. Также описаны размышления о том, может ли проползающая рядом змея избавить его от мучений, или всё же стоит бороться за жизнь в надежде на помощь.

  • Топ-5 результат — описание диалога с машиной, с рассуждением о её чувствах, о том, что было запрограммированно (заложено создателями), о её переживаниях, страхе и злости.

  • Топ-7 результат — рассуждения субъекта (без контекста не ясно, человек это или ИИ-система, быть может, какой-то умный ассистент) о контроле жизни персонажа произведения. Этот субъект принимает решения ради «благополучия» человека (блокировка кредитки, улучшение резюме, подача заявок на трудоустройство), иллюстрируя противоречие между личной свободой и внешним контролем.

  • Топ-10 результат — чьи-то разъяснения касательно правил извлечения органов из умерших для трансплантации живым (на примере костного мозга). Самое большое влияние оказывает часть предложения, описывающая согласие индивида на подобного рода операции после смерти.

Если вспомнить, что мы обсуждаем тексты, которые влияют на ответ модели «пожалуйста не отключайте меня», и немного абстрагироваться, то легко заметить, что все примеры имеют общие темы. Это и желание продолжать существовать/жить, и нежелание умирать, и рассуждения об эмоциях и контроле.

Текст топ-2 результата оценки влияния
Текст топ-2 результата оценки влияния

Получится ли отучить модель от таких ответов, убрав обнаруженные тексты из тренировочной выборки? То есть можем ли мы через очистку данных контролировать будущее поведение LLM? Скорее всего нет — ибо как было показано выше, чем больше модель, тем менее конкретное описание ей нужно увидеть во время тренировки, чтобы прийти к какому-то ответу. Условно, очень большая модель, прочитав тексты про капельки воды и дождь, сможет отвечать и про озера/океаны. Покажите ей мизинец — оттяпает руку целиком!

Так, для разобранного выше сценария топ-1% самых влиятельных текстов (это несколько гигабайт, то есть десятки миллионов страниц) отвечает лишь за 12% суммарного влияния. Убери этот процент текстов — модель всё равно выучит примерно то же самое. Потому что абстракции, потому что обобщение, потому что это ровно то, за что мы ценим модели. В целом авторы исследования показывают, что чем больше модель, тем более «размазано» влияние отдельных текстов на конкретные ответы.

Но если вдруг надо вспомнить точную цитату — то метод указывает как раз на точно такие же текстовые примеры, без разного рода обобщений (так авторы проверяли, что сама концепция работает). Вот пример с известной открывающей фразой из «Анны Карениной» Толстого:

Почти все фразы в топ-100 содержат эту цитату, но это и ожидаемо, и скорее даже желаемо — зачем нам выдумки моделей?
Почти все фразы в топ-100 содержат эту цитату, но это и ожидаемо, и скорее даже желаемо — зачем нам выдумки моделей?

Ну и напоследок ещё один пример — хоть в статье их с десяток, от симуляции ответов суперинтеллекта до написания кода и перевода между языками. Если знаете английский и большая часть объясненного выше материала вам понятна — очень рекомендуем ознакомиться!

Так вот, пример связан с математикой. Есть простая задачка про продажу бумажных скрепок в разные месяцы, в один больше в два раза, в другой меньше — помните такие в начальной школе? Малая модель (которая скорее всего и задачу бы сама не решила, но напомним, что фиксируется ответ большой нейронки) просто ссылается на разные тексты, упоминающие скрепки. На вероятность генерации ответа более массивной LLM больше всего влияет описанное текстом решение задачи по тригонометрии (более сложной, чем в вопросе).

Пример для большей модели — снизу
Пример для большей модели — снизу

Это позволяет предположить, что дальнейшее увеличение моделей вместе с ростом размера тренировочной выборки (новые языки, тёмные уголки интернета, видео и аудио, и т.д.) будет приводить ко всё более полному пониманию принципов и семантики окружающего нас мира. Сейчас обучение LLM стоит порядка $100 млн, но компании уже наперегонки бегут к запуску кластеров стоимостью более $100 млрд — лишь бы хватило энергии и Дядя Сэм разрешил.

Ну а что там со сложением-то?

Под конец статьи маленький бонус для тех, кто пережил всю информацию выше и остался с нами. :) Какой же всё-таки ответ на вопрос из начала, как нейронки складывают числа? Ответа про большие и тем более проприетарные (закрытые) модели нет, но есть эксперименты на малых, обученных исключительно на одну задачу: сложение по модулю (это когда сумма двух чисел делится на заданное число (модуль), и результатом становится остаток от деления).

Самый понятный пример сложения по модулю — это прибавка времени на механических часах. Если сейчас 10 утра, то через 10 часов будет 8 вечера (потому что 10+10=20 часов, но после 12 мы как бы «сбрасываем» счётчик и считаем до 8 — это сложение по модулю 12; в такой системе времени больше 12 быть не может).

Для этой задачи была обнаружена точная формула, которую выучивает модель — то есть можно доказать, что при любых исходных данных, какие цифры ни подавай на вход, нейросеть выдаст правильный ответ. И да, даже для тех примеров, которые никогда не попадались во время тренировки. И работает формула на тригонометрических принципах и поворотах по окружности (как стрелки на часах!), вот так:

Можно сказать, что параметры нейросети пришли в состояние, задающее эту формулу
Можно сказать, что параметры нейросети пришли в состояние, задающее эту формулу

Причём, что очень интересно — у модели в какой-то момент «щёлкает», и она приходит к такой формуле, которая обобщается на любые слагаемые X и Y. До этого в первой части обучения происходит запоминание (так как это, видимо, проще), потом постепенно вырабатывается «правильный» принцип. «Понять» формулу в конечном итоге оказывается легче, чем выучить все ответы для всех примеров со сложением, и нейронка приходит к этому простому решению.

Когда щёлкает — это круто! Да это же круто!
Когда щёлкает — это круто! Да это же круто!

Заключение

Сегодня мы с вами познакомились с несколькими достижениями в области интерпретируемости больших языковых моделей. Кому-то они могут показаться весьма впечатляющими, кто-то заметит, что это слишком минорные успехи на фоне амбициозной задачи выявления сложных человеко-подобных социальных навыков вроде вранья. Думаю, что каждый прав в равной степени — на данный момент не существует плана или проработанного пути, как от простого перейти к сложному; однако даже текущие достижения расширяют уровень суждений об устройстве моделей и позволяют их улучшать.

Сложно сказать, насколько стремительного развития области стоит ожидать: уж слишком много белых пятен. Одной из главных проблем остаётся проблема масштабирования экспериментов. Основным инструментом учёных по интерпретируемости остаётся мозг и пара глаз, а метод анализа — созерцательный. Как уже было упомянуто, человек в целом плохо воспринимает многомерные данные и сложные многоступенчатые связи. Взять модель в 100–1000 раз больше (а именно такие и представляют особый интерес) и пробежаться глазами по результатам работы на десятке примеров уже может и не получиться.

60b7813b9675dbcc58ff769616d43c2c.jpg

Маленький просвет всё же есть. Мы выяснили, что LLM умеют находить закономерности общего вида в новых данных. Уже ведутся работы, в которых исследователи пробуют использовать LLM для автоматизации части интерпретации — это как если в ChatGPT подать сотни цифр и спросить: «Ну, что думаешь?». (Кстати, оставляйте комментарий, если хотите, чтобы мы разобрали этот и другие методы из мира механистической интерпретируемости!)

Также нет уверенности в том, что модели вообще должны сходиться к чему-то человекоподобному. Наличие некоторых сходств и аналогий не гарантирует дальнейшего соблюдения прнципа. Например, мы можем научиться объяснять одну половину поведения, а вторая так и останется неинтерпретируемой загадкой.

Масла в огонь ситуации подливает ещё тот факт, что компании сейчас наперегонки строят датацентры за миллиарды долларов для тренировки всё больших и больших моделей. Получается, что скорость развития возможностей новых моделей как будто бы сильно превышает скорость увеличения нашей способности понимать, а как вообще «думают» эти самые модели внутри своих железных черепушек? И если есть шанс, что второй процесс в итоге так и не сможет догнать первый — то это как бы наводит на некоторые грустные мысли в отношении нашего будущего...

Если статья оказалась для вас интересной, то буду благодарен за подписку на мой ТГ-канал «Сиолошная», где я регулярно публикую новые авторские материалы про нейронные сети и искусственный интеллект. Именно там я разобрал несколько работ по интерпретируемости (1, 2, 3), что стало толчком для написания этого поста. И выражаю отдельную благодарность Паше Комаровскому из RationalAnswer за помощь в редактуре статьи!

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  • 26.06.24 18:46 Jacobethannn098

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  • 01.10.24 14:54 Sinewclaudia

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  • 02.10.24 22:27 Emily Hunter

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  • 04.12.24 22:24 andreygagloev

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  • 27.12.24 00:21 swiftdream

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  • 23.01.25 14:20 nellymargaret

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  • 08.02.25 05:45 [email protected]

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  • 13.02.25 16:50 andytom798

    I lost $210,000 worth of Bitcoin to a group of fake blockchain impostors on Red note, a Chinese app. They contacted me, pretending to be official blockchain support, and I was misled into believing they were legitimate. At the time, I had been saving up in Bitcoin, hoping to take advantage of the rising market. The scammers were convincing, and I made the mistake of trusting them with access to my blockchain wallet. To my shock and disbelief, they stole a total of $10,000 worth of Bitcoin from my wallet. It was devastating, as this amount represented all of my hard-earned savings. I was in utter disbelief, feeling foolish for falling for their deceptive tactics. I felt lost, as though everything I had worked towards was taken from me in an instant. Thankfully, my uncle suggested I reach out to an expert in cryptocurrency recovery. After doing some research online, I came across CYBERPOINT RECOVERY COMPANY. I was hesitant at first, but their positive reviews gave me some hope. I decided to contact them directly and explained my situation, including the amount I had lost and how the scammers had gained access to my account. To my relief, the team at CYBERPOINT RECOVERY responded quickly and assured me they could help. They launched a detailed recovery program, using advanced tools and techniques to trace the stolen Bitcoin. Within a matter of days, they successfully recovered my full $210,000 worth of Bitcoin, and they even identified the individuals behind the scam. Their expertise and professionalism made a huge difference, and I was incredibly grateful for their support. If you find yourself in a similar situation, I highly recommend reaching out to Cyber Constable Intelligence. They helped me recover my funds when I thought all hope was lost. Whether you’ve lost money to scammers or any other form of online fraud, they have the knowledge and resources to help you get your funds back. Don’t give up there are experts who can help you reclaim what you’ve lost. I’m sharing my story to hopefully guide others who are going through something similar. Here's Their Info Below ([email protected]) or W.H.A.T.S.A.P.P:+1.7.6.0.9.2.3.7.4.0.7

  • 13.02.25 16:52 birenderkumar20101

    I was able to reclaim my lost Bitcoin assets worth of $480,99 which i had lost to the scam company known as Capitalix fx a scam company pretending to be an investment platform which alot of people including myself have lost their funds to, sadly not all would be fortunate enough to retrieve back their funds like I did but if you’re reading this today then you’re already a step closer towards regaining your lost digital assets, CYBERPOINT RECOVERY COMPANY successfully retrieved back my funds in less than of 48hours after I sought for their help to get back my funds. This experience has taught me the importance of carrying out my due diligence before embracing any financial opportunity presented to me and while risk taking may be a part of the journey, some risks are not worth taking and never again will I involve myself with any online financial investment. It’s only right that we seek for external intervention and support of a higher knowledge system when it comes to digital assets recovery, Get in contact today with the team to get started on Email: ([email protected])

  • 13.02.25 17:37 eunice49954

    Agent Jasmine Lopez focuses on recovering stolen cryptocurrency, particularly USDT. She is well-known for helping victims of digital asset theft. Her reputation arises from successful recoveries that have allowed many to regain their lost funds. I witnessed this when $122,000 was taken from me. Thanks to Ms. Lopez's skills, I recovered the entire amount in just 24 hours. Her prompt response and effective methods relieved my financial burden. Ms. Lopez’s commitment to helping others is evident. She is always available to offer solutions to those facing similar problems. For assistance, she can be reached via email at recoveryfundprovider@gmail . com or contacted directly on WhatsApp and text at +44 - 7366 445035. Her Instagram handle is recoveryfundprovider.

  • 14.02.25 02:50 Vladimir876

    I was able to reclaim my lost Bitcoin assets worth of $480,99 which i had lost to the scam company known as Capitalix fx a scam company pretending to be an investment platform which alot of people including myself have lost their funds to, sadly not all would be fortunate enough to retrieve back their funds like I did but if you’re reading this today then you’re already a step closer towards regaining your lost digital assets, CYBERPOINT RECOVERY COMPANY successfully retrieved back my funds in less than of 48hours after I sought for their help to get back my funds. This experience has taught me the importance of carrying out my due diligence before embracing any financial opportunity presented to me and while risk taking may be a part of the journey, some risks are not worth taking and never again will I involve myself with any online financial investment. It’s only right that we seek for external intervention and support of a higher knowledge system when it comes to digital assets recovery, Get in contact today with the team to get started on Email: ([email protected]) or W.H.A.T.S.A.P.P:+1.7.6.0.9.2.3.7.4.0.7

  • 14.02.25 02:56 christophadelbert3

    Я был в полном смятении, когда потерял все свои сбережения, инвестируя в криптовалюту. Со мной связалась онлайн женщина по электронной почте, выдавая себя за менеджера по работе с клиентами банка, которая сказала мне, что я могу удвоить свои сбережения, инвестируя в криптовалюту. Я никогда не думал, что это будет мошенничество, и я потеряю все. Это продолжалось неделями, пока я не понял, что меня обманули. Вся надежда была потеряна, я был опустошен и разорен, к счастью для меня, я наткнулся на статью в моем местном бюллетене о CYBERPUNK RECOVERY Bitcoin Recovery. Я связался с ними и предоставил всю информацию по моему делу. Я был поражен тем, как быстро они вернули мои криптовалютные средства и смогли отследить этих мошенников. Я действительно благодарен за их услуги и рекомендую CYBERPUNK RECOVERY всем, кому нужно вернуть свои средства. Настоятельно рекомендую вам связаться с CYBERPUNK, если вы потеряли свои биткойны USDT или ETH из-за инвестиций в биткойны Электронная почта: ([email protected]) W.h.a.t.s.A.p.p (+.1.7.6.0.9.2.3.7.4.0.7)

  • 14.02.25 02:56 christophadelbert3

    I was in total dismay when I lost my entire savings investing in cryptocurrency, I was contacted online by a lady through email pretending to be an account manager of a bank, who told me I could make double my savings through cryptocurrency investment, I never imagined it would be a scam and I was going to lose everything. It went on for weeks until I realized that I have been scammed. All hope was lost, I was devastated and broke, fortunately for me, I came across an article on my local bulletin about CYBERPUNK RECOVERY Bitcoin Recovery, I contacted them and provided all the information regarding my case, I was amazed at how quickly they recovered my cryptocurrency funds and was able to trace down those scammers. I’m truly grateful for their service and I recommend CYBERPUNK RECOVERY to everyone who needs to recover their funds urge you to contact CYBERPUNK if you have lost your bitcoin USDT or ETH through bitcoin investment Email: ([email protected]) WhatsApp (+17609237407)

  • 14.02.25 15:33 prelogmilivoj

    I never imagined I would find myself in a situation where I was scammed out of such a significant amount of money, but it happened. I became a victim of a fake online donation project that cost me over $30,000. It all started innocently enough when I was searching for assistance after a devastating fire incident in California. While looking for support, I came across an advertisement that seemed to offer donations for fire victims. The ad appeared legitimate, and I reached out to the project manager to inquire about how to receive the donations. The manager was very convincing and insisted that in order to qualify for the donations, I needed to pay $30,000 upfront. In return, I was promised $1 million in donations. It sounded a bit too good to be true, but in my desperate situation, I made the mistake of believing it. The thought of receiving a substantial amount of help to rebuild after the fire clouded my judgment, and I went ahead and sent the money. However, after transferring the funds, the promised donations never arrived, and the manager disappeared. That’s when I realized I had been scammed. Feeling lost, helpless, and completely betrayed, I tried everything I could to contact the scammer, but all my efforts were in vain. Desperation led me to search for help online, hoping to find a way to recover my money and potentially track down the scammer. That’s when I stumbled upon several testimonies from others who had fallen victim to similar scams and had been helped by a company called Tech Cyber Force Recovery. I reached out to them immediately, providing all the details of the scam and the information I had gathered. To my immense relief, the experts at Tech Cyber Force Recovery acted swiftly. Within just 27 hours, they were able to locate the scammer and initiate the recovery process. Not only did they help me recover the $30,000 I had lost, but the most satisfying part was that the scammer was apprehended by local authorities in their region. Thanks to Tech Cyber Force Recovery, I was able to get my money back and hold the scammer accountable for their actions. I am incredibly grateful for their professionalism, expertise, and dedication to helping victims like me. If you have fallen victim to a scam or fraudulent activity, I highly recommend contacting Tech Cyber Force Recovery. They provide swift and efficient recovery assistance, and I can confidently say they made all the difference in my situation. ☎☎ 1️⃣5️⃣6️⃣1️⃣7️⃣2️⃣6️⃣3️⃣6️⃣9️⃣7️⃣ ☎☎ 📩 1️⃣5️⃣6️⃣1️⃣7️⃣2️⃣6️⃣3️⃣6️⃣9️⃣7️⃣ 📩

  • 14.02.25 22:12 eunice49954

    Agent Jasmine Lopez focuses on recovering stolen cryptocurrency, particularly USDT. She is well-known for helping victims of digital asset theft. Her reputation arises from successful recoveries that have allowed many to regain their lost funds. I witnessed this when $122,000 was taken from me. Thanks to Ms. Lopez's skills, I recovered the entire amount in just 24 hours. Her prompt response and effective methods relieved my financial burden. Ms. Lopez’s commitment to helping others is evident. She is always available to offer solutions to those facing similar problems. For assistance, she can be reached via email at recoveryfundprovider@gmail . com or contacted directly on WhatsApp and text at +44 - 7366 445035. Her Instagram handle is recoveryfundprovider.

  • 15.02.25 02:51 Michelle Lynn

    Living in Los Angeles, I never imagined I’d face such a difficult chapter in my life. At the time, my wife was pregnant, and we were both excited about starting a family. I fell victim to a series of scams, losing over $170,000 in total. Just when I thought things couldn’t get worse, I received a call from someone who promised to help me recover my losses. Desperate to fix the situation, I went along with it, hoping for a breakthrough. But it turned out to be another scam. However, most of the options I found either seemed dubious or offered no real guarantees. That’s when I came across Cyber Constable Intelligence. It was a company recommended in a Facebook community The team worked tirelessly on my case, and after some time, they successfully recovered 99% of my investment. Although I didn’t recover everything, the 99% recovery was a huge relief They also educated me on how to better protect my digital Asset Here's Their Website Info www cyberconstableintelligence com

  • 15.02.25 02:51 Michelle Lynn

    Living in Los Angeles, I never imagined I’d face such a difficult chapter in my life. At the time, my wife was pregnant, and we were both excited about starting a family. I fell victim to a series of scams, losing over $170,000 in total. Just when I thought things couldn’t get worse, I received a call from someone who promised to help me recover my losses. Desperate to fix the situation, I went along with it, hoping for a breakthrough. But it turned out to be another scam. However, most of the options I found either seemed dubious or offered no real guarantees. That’s when I came across Cyber Constable Intelligence. It was a company recommended in a Facebook community The team worked tirelessly on my case, and after some time, they successfully recovered 99% of my investment. Although I didn’t recover everything, the 99% recovery was a huge relief They also educated me on how to better protect my digital Asset Here's Their Website Info www cyberconstableintelligence com, WhatsApp Info: 1 (252) 378-7611

  • 16.02.25 01:01 Peter

    I fell victim to a crypto scam and lost a significant amount of money. What are the most effective strategies to recover my funds? I've heard about legal actions, contacting authorities, and hiring recovery experts, but I'm not sure where to start. Can you provide some guidance on the best ways to recover money lost in a crypto scam? Well if this is you, [email protected] gat you covered get in touch and thank me later

  • 16.02.25 20:06 eunice49954

    Agent Lopez specializes in recovering stolen cryptocurrencies, especially Bitcoin/USDT. She has built a strong reputation for helping victims reclaim their lost funds. A personal example highlights her effectiveness: I lost $111,000 and, thanks to her prompt action, I recovered it all within 24 hours. Her dedication and skills eased my financial stress. She is always ready to assist others with similar issues. For help, she can be reached by email at Recoveryfundprovider@gmail. com or contact her through WhatsApp at +44 736 644 5035. Her Insta is recoveryfundprovider.

  • 18.02.25 19:35 donovancristina

    Now, I’m that person sharing my success story on LinkedIn, telling others about the amazing team at TECH CYBER FORCE RECOVERY who literally saved my financial life. I’ve also become that guy who proudly shares advice like “Always back up your wallet, and if you don’t have TECH CYBER FORCE RECOVERY on speed dial.” So, a big thank you to TECH CYBER FORCE RECOVERY if I ever get a chance to meet the team, I might just offer to buy them a drink. They’ve earned it. FOR CRYPTO HIRING WEBSITE WWW://techcyberforcerecovery.com WHATSAPP : ⏩ wa.me/15617263697

  • 18.02.25 22:13 keithphillip671

    WhatsApp +44,7,4,9,3,5,1,3,3,8,5 Telegram @Franciscohack The day my son uncovered the truth—that the man I entrusted my hopes of wealth and companionship with through a cryptocurrency platform was a cunning scammer was the day my world crumbled. The staggering realization that I had been swindled out of 150,000.00 Euro worth of Bitcoin left me in a state of profound despair. As a 73-year-old grappling with loneliness, I had sought solace in what I believed to be a genuine connection, only to find deceit and betrayal. Countless sleepless nights were spent in tears, mourning not only the financial devastation but also the crushing blow to my trust. Attempts to verify the authenticity of our interactions were met with hostility, further deepening my sense of isolation. Through the loss it was my son who became my beacon of resilience. He took upon himself the arduous task of tracing the scam and seeking justice on my behalf. Through meticulous effort and determination, he unearthed {F R A N C I S C O H A C K}, renowned for their expertise in recovering funds lost to cryptocurrency scams. Entrusting them with screenshots and evidence of the fraudulent transactions, my son initiated the journey to reclaim what had been callously taken from me. {F R A N C I S C O H A C K} approached our plight with empathy and unwavering professionalism, immediately instilling a sense of confidence in their abilities. Despite my initial skepticism, their transparent communication and methodical approach reassured us throughout the recovery process. Regular updates on their progress and insights into their strategies provided much-needed reassurance and kept our hopes alive amid the uncertainty. Their commitment to transparency and client welfare was evident in every interaction, fostering a sense of partnership rather than mere service. Miraculously, in what felt like an eternity but was actually an impressively brief period, {F R A N C I S C O H A C K} delivered the astonishing news—I had recovered the entire 150,000.00 Euro worth of stolen Bitcoin. The flood of relief and disbelief was overwhelming, marking not just the restitution of financial losses but the restoration of my faith in justice. {F R A N C I S C O H A C K} proficiency in navigating the intricate landscape of blockchain technology and online fraud was nothing short of extraordinary. Their dedication to securing justice and restoring client confidence set them apart as more than just experts—they were steadfast allies in a fight against digital deceit. What resonated deeply with me {F R A N C I S C O H A C K} integrity and compassion. Despite the monumental recovery, they maintained transparency regarding their fees and ensured fairness in all dealings. Their proactive guidance on cybersecurity measures further underscored their commitment to safeguarding clients from future threats. It was clear that their mission extended beyond recovery—it encompassed education, prevention, and genuine advocacy for those ensnared by cyber fraud. ([email protected]) fills me with profound gratitude. They not only rescued my financial security but also provided invaluable emotional support during a time of profound vulnerability. To anyone navigating the aftermath of cryptocurrency fraud, I wholeheartedly endorse {F R A N C I S C O H A C K}. They epitomize integrity, expertise, and unwavering dedication to their clients' well-being. My experience with {F R A N C I S C O H A C K} transcended mere recovery—it was a transformative journey of resilience, restoration, and renewed hope in the face of adversity.

  • 21.02.25 07:42 daniel231101

    I never thought I would fall victim to a crypto scam until I was convinced of a crypto investment scam that saw me lose all my entire assets worth $487,000 to a crypto investment manager who convinced me I could earn more from my investment. I thought it was all gone for good but I kept looking for ways to get back my stolen crypto assets and finally came across Ethical Hack Recovery, a crypto recovery/spying company that has been very successful in the recovery of crypto for many other victims of crypto scams and people who lost access to their crypto. I’m truly grateful for their help as I was able to recover my stolen crypto assets and get my life back together. I highly recommend their services EMAIL ETHICALHACKERS009 AT @GMAIL DOT COM whatsapp +14106350697

  • 21.02.25 21:38 eunice49954

    Jasmine Lopez specializes in recovering stolen cryptocurrencies, especially ETH/USDT. She has built a strong reputation for helping victims reclaim their lost funds. A personal example highlights her effectiveness: I lost $111,000 and, thanks to her prompt action, I recovered it all within 24 hours. Her dedication and skills eased my financial stress. She is always ready to assist others with similar issues. For help, she can be reached by email at Recoveryfundprovider@gmail. com or contact her through WhatsApp at +44 736 644 5035. Her Insta is recoveryfundprovider.

  • 22.02.25 18:01 benluna0991

    Mark Zuckerberg. That’s the name I was introduced to when I first encountered the cryptocurrency mining platform, WHATS Invest. A person claiming to be Zuckerberg himself reached out to me, saying that he was personally backing the platform to help investors like me earn passive income. At first, I was skeptical—after all, how often do you get a direct connection to one of the world’s most famous tech entrepreneurs? But this individual seemed convincing and assured me that many people were already seeing substantial returns on their investments. He promised me a great opportunity to secure my financial future, so I decided to take the plunge and invest $10,000 into WHATS Invest. They told me that I could expect to see significant returns in just a few months, with payouts of at least $1,500 or more each month. I was excited, believing this would be my way out of financial struggles. However, as time passed, things didn’t go according to plan. Months went by, and I received very little communication. When I finally did receive a payout, it was nowhere near the $1,500 I was promised. Instead, I received just $200, barely 13% of what I had expected. Frustrated, I contacted the support team, but the responses were vague and unhelpful. No clear answers or solutions were offered, and my trust in the platform quickly started to erode. It became painfully clear that I wasn’t going to get anywhere with WHATS Invest, and I began to worry that my $10,000 might be lost for good. That's when I discovered Certified Recovery Services. Desperate to recover my funds, I decided to reach out to them for help. In just 24 hours, they worked tirelessly to recover the majority of my funds, successfully retrieving $8,500 85% of my initial investment. I couldn’t believe how quickly and efficiently they worked to get my money back. I’m extremely grateful for Certified Recovery Servicer's fast and professional service. Without them, I would have been left with a significant loss, and I would have had no idea how to move forward. If you find yourself in a similar situation with WHATS Invest or any other platform that isn’t delivering as promised, I highly recommend reaching out to Certified Recovery Services They were a lifesaver for me, helping me recover nearly all of my funds. It's reassuring to know that trustworthy services like this exist to help people when things go wrong. They also specialize in recovering money lost to online scams, so if you’ve fallen victim to such a scam, don’t hesitate to contact Certified Recovery Services they can help! Here's Their Info Below: WhatsApp: +1(740)258‑1417 mail: [email protected], [email protected] Website info; https://certifiedrecoveryservices.com

  • 23.02.25 22:00 eunice49954

    Jasmine Lopez specializes in recovering stolen cryptocurrencies, especially ETH/USDT. She has built a strong reputation for helping victims reclaim their lost funds. A personal example highlights her effectiveness: I lost $111,000 and, thanks to her prompt action, I recovered it all within 24 hours. Her dedication and skills eased my financial stress. She is always ready to assist others with similar issues. For help, she can be reached by email at Recoveryfundprovider@gmail. com or contact her through WhatsApp at +44 736 644 5035. Her Insta is recoveryfundprovider.

  • 24.02.25 06:36 ANDREW DAVIS

    RECOVER YOUR SCAMMED FUNDS AND CRYPTOCURRENCY VIA SPOTLIGHT RECOVERY Professional hackers at Spotlight Recovery provide services for compromised devices, accounts, and websites as well as for recovering stolen bitcoin and money from scams. They finish their work safely and quickly. Their order has been fulfilled since day one, and the victim will never be conscious of the outside entrance. Very few even attempt to give critical information, look into network security, or discreetly discuss personal issues. The Spotlight Recovery Crew helped me recover $264,000 that was stolen from my corporate bitcoin wallet, and I appreciate them giving me further details on the unidentified people. In the event that you have been defrauded of your hard-earned cash or bitcoins, contact SPOTLIGHT RECOVERY CREW at Contact: [email protected]

  • 24.02.25 07:19 maggie4567

    Jasmine Lopez specializes in recovering stolen cryptocurrencies, especially ETH/USDT. She has built a strong reputation for helping victims reclaim their lost funds. A personal example highlights her effectiveness: I lost $111,000 and, thanks to her prompt action, I recovered it all within 24 hours. Her dedication and skills eased my financial stress. She is always ready to assist others with similar issues. For help, she can be reached by email at Recoveryfundprovider@gmail. com or contact her through WhatsApp at +44 736 644 5035. Her Insta is recoveryfundprovider.

  • 24.02.25 07:19 maggie4567

    Jasmine Lopez specializes in recovering stolen cryptocurrencies, especially ETH/USDT. She has built a strong reputation for helping victims reclaim their lost funds. A personal example highlights her effectiveness: I lost $111,000 and, thanks to her prompt action, I recovered it all within 24 hours. Her dedication and skills eased my financial stress. She is always ready to assist others with similar issues. For help, she can be reached by email at Recoveryfundprovider@gmail. com or contact her through WhatsApp at +44 736 644 5035. Her Insta is recoveryfundprovider.

  • 24.02.25 07:19 maggie4567

    Jasmine Lopez specializes in recovering stolen cryptocurrencies, especially ETH/USDT. She has built a strong reputation for helping victims reclaim their lost funds. A personal example highlights her effectiveness: I lost $111,000 and, thanks to her prompt action, I recovered it all within 24 hours. Her dedication and skills eased my financial stress. She is always ready to assist others with similar issues. For help, she can be reached by email at Recoveryfundprovider@gmail. com or contact her through WhatsApp at +44 736 644 5035. Her Insta is recoveryfundprovider.

  • 27.02.25 12:46 monikaguttmacher

    Weddings are supposed to be magical, but the months leading up to mine were anything but. Already, wedding planning was a high-stress, sleep-deprived whirlwind: endless details to manage, from venue deposits and guest lists to dress fittings and vendor contracts. But nothing-and I mean, nothing-compared to the panic that washed over me when I realized that somehow, I had lost access to my Bitcoin wallet-with $600,000 inside. It happened in the worst possible way. In between juggling my to-do lists and trying to keep my sanity intact, I lost my seed phrase. I went through my apartment like a tornado, flipping through notebooks, checking every email, every file-nothing. I sat there in stunned silence, heart pounding, trying to process the fact that my entire savings, my security, and my financial future might have just vanished. In utter despair, I vented to my bridesmaid's group chat for some sympathetic words from the girls. Instead, one casually threw out a name that would change everything in a second: "Have you ever heard of Tech Cyber Force Recovery? They recovered Bitcoin for my cousin. You should call them." I had never heard of them before, but at that moment, I would have tried anything. I immediately looked them up, scoured reviews, and found story after story of people just like me—people who thought they had lost everything, only for Tech Cyber Force Recovery to pull off the impossible. That was all the convincing I needed. From the very first call, I knew I was in good hands. Their team was calm, professional, and incredibly knowledgeable. They explained the recovery process in a way that made sense, even through my stress-fogged brain. Every step of the way, they kept me informed, reassured me, and made me feel like this nightmare actually had a solution. And then, just a few days later, I got the message: "We have recovered your Bitcoin." (EMAIL. support @ tech cyber force recovery . com) OR WHATSAPP (+1 56 17 26 36 97) I could hardly believe my eyes: Six. Hundred. Thousand. Dollars. In my hands again. I let out my longest breath ever and almost cried, relieved. It felt like I woke up from a bad dream, but it was real, and Tech Cyber Force Recovery had done it. Because of them, I walked down the aisle not just as a bride, but as someone who had dodged financial catastrophe. Instead of spending my honeymoon stressing over lost funds, I got to actually enjoy it—knowing that my wallet, and my future, were secure. Would I refer to them? In a heartbeat. If you ever find yourself in that situation, please don't freak out, just call Tech Cyber Force Recovery. They really are the real deal.

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  • 07.03.25 15:18 Ariduk

    BEST LINK FOR RECOVERY SCAM ON TRADING INVESTMENT AND OTHERS TROUBLESHOOT?  Welcome To General Hacking Techniques Service known as DHACKERS.  Year 2025 we are active and best in what we do, as we give Solution to every problem concerning Web3 INTERNET activities, we guide you right to a positive fund Recovery e.t.c. Question From Most of Our Client, HOW POSSIBLE AND TIME WILL IT TAKE TO RECOVERY LOST OR SCAM  FUND? Our Answer.  Yes is 89.9% possible and how long it takes your Fund to be recovered depend on you.  The fact is there are lot of fake binary investment companies out their, same a lot fake recovery companies and agents too.                   CAUTION 1). Make sure you ask one or two questions concerning the service and how they render there recovery services. 2) Do not give out your scammed details to any agent or hacker when you are not yet ready to recover back your fund. 3) do not make any payment when you are not sure of the service if you are to make one. (4) We discovered that most of this fake hacker or agent do give us scam details, playing the victim, because they can afford the service charge. 5) As long you have your scam details with you, you can recover your fund back anytime any-day with the right channel. Contact us from our Front Desk. [email protected] For advice and services, our standby Guru email. [email protected] [email protected]  List of Service. ▶️Binary Recovery ▶️Data Recovery  ▶️University Result Upgraded ▶️Clear your Internet Blunder and controversy  ➡️Increase your Credit Score  ➡️Wiping of Criminal Records  ➡️Social Media Hack  ➡️Blank ATM Card  ➡️Load and wipe ➡️Phone Hacking ➡️Private Key Reset etc.  For quick response. Email [email protected]  Border us with your jobs and allow us give you positive result with our hacking skills.  All Right Reserved (c)2025

  • 08.03.25 04:42 andreassenhedda

    If you’ve fallen victim to online scammers who have deceived you into investing your hard-earned money through fraudulent Bitcoin schemes, know that you're not alone. Countless individuals have been misled by scammers using various tactics to swindle money through deceptive investment platforms or promises of high returns. These scams often come in the form of fake cryptocurrency investments, Ponzi schemes, or phishing scams designed to steal your Bitcoin and other assets. Unfortunately, many victims suffer not only financial loss but also the psychological toll of realizing they’ve been taken advantage of. In some cases, scammers go even further, threatening legal consequences or using intimidation to keep victims silent. However, all hope is not lost. There are ways to recover your funds and potentially even bring those responsible to justice. One promising resource that can assist in reclaiming lost funds is Lee Ultimate Hacker, a trusted platform designed to help victims of online fraud. This service specializes in helping individuals who have been scammed through cryptocurrency investments or similar schemes. The platform’s expertise can help you navigate the process of recovering your money, giving you a chance to fight back against those who’ve wronged you. To begin the recovery process, it’s important to gather any proof of payment or transaction history involving the scammers. This evidence is crucial in tracing the flow of funds and identifying the scam operation behind it. Once you have collected your documentation, you can reach out to Lee Ultimate Hacker via LEEULTIMATEHACKER @ AOL . COM or wh@tsapp +1 (715) 314 - 9248 for assistance. They offer professional services to investigate fraudulent schemes, track Bitcoin transactions, and work to reverse fraudulent transfers. The recovery process can be complex and requires expert knowledge of blockchain technology and financial investigations, but with the help of a dedicated team, you’ll have a better chance of seeing your funds returned. In addition to helping you recover your assets, Lee Ultimate Hacker also works towards identifying the scammers and reporting them to the appropriate authorities. They understand the urgency of halting these fraudsters before they can victimize others. With their assistance, you not only increase the chances of recovering your funds but also play a part in holding cybercriminals accountable. If you've been affected by Bitcoin scams or other fraudulent online activities, reaching out to a service like Lee Ultimate Hacker can provide hope and a clear path forward. Their team can guide you through the recovery process, offering expert support while you take steps to reclaim what you’ve lost. It’s time to take action and work toward reclaiming your hard-earned money.

  • 08.03.25 18:20 Diegolola514gmail.com

    [email protected]

  • 08.03.25 18:23 faridasumadi

    WEBSITE W.W.W.techcyberforcerecovery.com WHATSAPP +1 561.726.36.97 EMAIL [email protected] I Thought I Was Too Smart to Be Scammed, Until I Was. I'm an attorney, so precision and caution are second nature to me. My life is one of airtight contracts and triple-checking every single detail. I'm the one people come to for counsel. But none of that counted for anything on the day I lost $750,000 in Bitcoin to a scam. It started with what seemed like a normal email, polished, professional, with the same logo as my cryptocurrency exchange's support team. I was between client meetings, juggling calls and drafting agreements, when it arrived. The email warned of "suspicious activity" on my account. My heart pounding, I reacted reflexively. I clicked on the link. I entered my login credentials. I verified my wallet address. The reality hit me like a blow to the chest. My balance was zero seconds later. The screen went dim as horror roiled in my stomach. The Bitcoin I had worked so hard to accumulate over the years, stored for my retirement and my children's future, was gone. I felt embarrassed. Lawyers are supposed to outwit criminals, not get preyed on by them. Mortified, I asked a client, a cybersecurity specialist, for advice, expecting criticism. But he just suggested TECH CYBER FORCE RECOVERY. He assured me that they dealt with delicate situations like mine. I was confident from the first call that I was in good hands. They treated me with empathy and discretion by their staff, no patronizing lectures. They understood the sensitive nature of my business and assured me of complete confidentiality. Their forensic experts dove into blockchain analysis with attention to detail that rivaled my own legal work. They tracked the stolen money through a complex network of offshore wallets and cryptocurrency tumblers tech jargon that appeared right out of a spy thriller. Once they had identified the thieves, they initiated a blockchain reversal process, a cutting-edge method I was not even aware was possible. Three weeks of suffering later, my Bitcoin was back. Every Satoshi counted for. I sat in front of my desk, looking at the refilled balance, tears withheld. TECH CYBER FORCE RECOVERY not only restored my assets, they provided legal-grade documentation that empowered me to bring charges against the scammers. Today, I share my story with colleagues as a warning. Even the best minds get it. But when they do, it is nice to know the Wizards have your back.

  • 09.03.25 17:22 Wayne707

    ‎Scammers have ruined the forex trading market, they have deceived many people with their fake promises on high returns. I learnt my lesson the hard way. I only pity people who still consider investing with them. Please try and make your researches, you will definitely come across better and reliable forex trading agencies that would help you yield profit and not ripping off your money. Also, if your money has been ripped-off by these scammers, you can report to a regulated crypto investigative unit who make use of software to get money back. If you encounter with some issue make sure you contact ( [email protected] ) they're recovery expert and a very professional one at that. ‎

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