Olapa-MCoT: Enhancing the Chinese Mathematical Reasoning Capability of LLMs
This work addresses the challenge of enhancing mathematical reasoning capabilities in Chinese for LLM users, representing an incremental improvement with specific gains.
The authors tackled the problem of improving Chinese mathematical reasoning in large language models by developing Olapa-MCoT, a model based on llama2-13B, which achieved a 50% accuracy in Chinese mathematical reasoning—a 36% increase over the base model.
CoT (Chain-of-Thought) is a way to solve reasoning problems for LLMs . Recently, many researches appear for improving the CoT capability of LLMs. In this work, we also proposed Olapa-MCoT, which is a LLMs based on llama2-13B PLM for finetuning and alignment learning. During the alignment training, we proposed the SimRRHF algorithm and Incorrect Data Relearning and mainly focused on optimizing the Chinese mathematical reasoning ability of Olapa-MCoT. The experiment achieved significant results, with the accuracy of Chinese mathematical reasoning up to 50%, 36% rise compared to llama2-13B. In addition, the accuracy of English reasoning ability also increased by nearly 4%.