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  4. T. 514, № 2, 2023

Доклады Российской академии наук. Математика, информатика, процессы управления, 2023, T. 514, № 2, стр. 262-269

БОЛЬШИЕ ЯЗЫКОВЫЕ МОДЕЛИ ДЛЯ СЛЕДОВАНИЯ ИНСТРУКЦИЯМ НА РУССКОМ ЯЗЫКЕ: МОДЕЛИ И ДАТАСЕТЫ С ОТКРЫТОЙ ЛИЦЕНЗИЕЙ ДЛЯ КОММЕРЧЕСКОГО ИСПОЛЬЗОВАНИЯ

Д. Косенко 1, 3, *, Ю. Куратов 1, 2, 3, **, Д. Жарикова 3, ***

1 Московский физико-технический институт
Москва, Россия

2 AIRI
Москва, Россия

3 DeepPavlov.ai
Москва, Россия

* E-mail: dimweb.tech@mail.ru
** E-mail: yurii.kuratov@phystech.edu
*** E-mail: dilyara.baymurzina@phystech.edu

Поступила в редакцию 31.08.2023
После доработки 30.09.2023
Принята к публикации 15.10.2023

Аннотация

В данной статье представлен подход к разработке и дообучению больших языковых моделей для русского языка, способных следовать инструкциям в различных доменах. В качестве базовых моделей использованы XGLM-4.5B, LLaMA-1 7B, LLaMA-1 13B, LLaMA-2 7B, LLaMA-2 13B, ruGPT-3.5 13B. В данной работе проводятся сравнения двух основных методик дообучения: дообучение всех параметров модели и дообучение с использованием LoRA слоев. Для создания датасета для дообучения модели использованы несколько открытых источников данных на английском языке, таких как Databricks Dolly 15k, OpenAssistant Conversations Dataset (OASST1), chip2-instruct-alpha-v6a-1, которые затем были переведены на русский язык с помощью модели WMT 21 En-X с лицензией MIT. В данной работе показано, что качество предоставляемых для обучения инструкций существенно влияет на способность решения задач на автоматических метриках качества MT-BENCH и MMLU. При этом качество моделей, обученных на собранном в рамках работы датасете с коммерческой лицензией, достигает сравнимых результатов с моделями, дообученными на датасете Saiga с ограниченной лицензией. Дообученные языковые модели и собранный набор данных для русского языка выложены в открытый доступ с лицензиями, подходящими для коммерческого использования.1

Ключевые слова: большие языковые модели, языковые модели, языковые модели для русского языка

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