使用基於模板的數據生成訓練和評估語言模型

Training and Evaluating Language Models with Template-based Data Generation

November 27, 2024

作者: Yifan Zhang

cs.AI

摘要

大型語言模型（LLMs）如GPT-3、PaLM和Llama的快速發展顯著改變了自然語言處理，展示出在理解和生成語言方面的卓越能力。然而，這些模型在需要複雜推理的任務中通常遇到困難，特別是在數學問題解決方面，部分原因是缺乏用於訓練複雜推理能力所需的大規模、高質量、特定領域的數據集。為了解決這一限制，我們引入了基於模板的數據生成（TDG）方法，這是一種新穎的方法，利用LLMs（GPT-4）自動生成參數化的元模板，然後用於合成各種高質量問題和解決方案。利用TDG，我們創建了TemplateMath Part I: TemplateGSM數據集，包括超過700萬個合成生成的小學數學問題，每個問題都附有基於代碼和自然語言的解決方案，並具有生成無限數量問題的潛力。這個數據集緩解了大規模數學數據集的稀缺問題，並為LLMs在數學推理中的預訓練、微調和評估提供了寶貴資源。我們的方法不僅能夠生成幾乎無限的數據，還通過使用GPT-4進行元模板生成，將數據擴增提升到一個新水平，確保多樣且高質量的問題結構。TemplateMath Part I: TemplateGSM數據集可在https://huggingface.co/datasets/math-ai/TemplateGSM公開獲得。代碼可在https://github.com/iiis-ai/TemplateMath獲得。

English

The rapid advancement of large language models (LLMs) such as GPT-3, PaLM, and Llama has significantly transformed natural language processing, showcasing remarkable capabilities in understanding and generating language. However, these models often struggle with tasks requiring complex reasoning, particularly in mathematical problem-solving, due in part to the scarcity of large-scale, high-quality, domain-specific datasets necessary for training sophisticated reasoning abilities. To address this limitation, we introduce Template-based Data Generation (TDG), a novel approach that leverages LLMs (GPT-4) to automatically generate parameterized meta-templates, which are then used to synthesize a vast array of high-quality problems and solutions. Leveraging TDG, we create TemplateMath Part I: TemplateGSM, a dataset comprising over 7 million synthetically generated grade school math problems--each accompanied by code-based and natural language solutions--with the potential to generate an effectively unlimited number more. This dataset alleviates the scarcity of large-scale mathematical datasets and serves as a valuable resource for pre-training, fine-tuning, and evaluating LLMs in mathematical reasoning. Our method not only enables the generation of virtually infinite data but also elevates data augmentation to a new level by using GPT-4 for meta-template generation, ensuring diverse and high-quality problem structures. The TemplateMath Part I: TemplateGSM dataset is publicly available at https://huggingface.co/datasets/math-ai/TemplateGSM. The code is available at https://github.com/iiis-ai/TemplateMath.