Olapa-MCoT：增强大语言模型的中文数学推理能力
Olapa-MCoT: Enhancing the Chinese Mathematical Reasoning Capability of LLMs
AI总结：
本研究针对Llama-2-13B中文数学推理能力弱的问题，提出Olapa-MCoT方法，通过SimRRHF和IDRL提升模型性能，使中文推理准确率升36%，且可适配任意主流LLMs。
摘要：
过去两年，ChatGPT在多语言和多任务上的出色表现使大语言模型（LLMs）受到广泛关注。但受限于高昂成本，许多研究只能聚焦于单一主要语言的能力。如何在有限数据和算力下快速提升模型在新语言上的能力，同时不降低其原有能力？本研究聚焦于提升在中文数学推理上表现较弱的Llama-2-13B的中文数学推理能力，提出了数学思维链方法（Olapa-MCoT）。首先，我们提出相似性迭代排名人类反馈（SimRRHF），该方法基于迭代排名人类反馈（RRHF）引入相似性损失，从而对模型优化方向施加约束。此外，我们设计了新颖的错误数据重学习（IDRL）方法，以提升模型学习困难知识的能力。实验取得了显著性能，中文数学推理准确率最高达50%，相比Llama-2-13B-chat提升了36%；此外，英文推理能力的准确率也提升了近4%。值得一提的是，我们的方法可应用于任何主流大语言模型。
英文摘要：
In the past two years, the outstanding performance of ChatGPT in multilingual and multitasking has led to large language models (LLMs) attracting widespread attention. However, restricted by expensive costs, many studies have to focus on the ability of only one major language. How can we quickly improve the model's capabilities in new languages without reducing its original capabilities under limited data and computing power? In this work, we focus on improving the Chinese mathematical reasoning capability based on Llama-2-13B, which is weak in Chinese mathematical reasoning. We proposed the Mathematical Chain of Thought method (Olapa-MCoT). First, we propose Similarity RRHF (SimRRHF), which adds the constraint of model optimization direction by introducing similarity loss based on RRHF. Furthermore, the novelty Incorrect Data Relearning (IDRL) method is designed, which improves the model's ability to learn difficult knowledge. The experiment achieves significant performance, with the accuracy of Chinese mathematical reasoning up to 50%, a 36% rise compared to Llama-2-13B-chat. In addition, the accuracy of English reasoning ability also increased by nearly 4%. It is worth mentioning that our method can be applied to any language major LLMs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
