Yixin Liu, Avi Singh, C. Daniel Freeman, John D. Co-Reyes, Peter J. Liu
TL;DR尽管大型语言模型在许多自然语言任务中取得了成功,但解决数学问题仍然是一个巨大的挑战。本研究通过使用 MATH 数据集,探索了三种微调策略,即解决方案微调、解决方案聚类重新排序和多任务顺序微调,并发现这些方法可以显著提高模型的性能。
Abstract
Despite their success in many natural language tasks, solving math problems remains a significant challenge for large language models (LLMs). A large gap exists between LLMs' pass-at-one and pass-at-N performance