arXiv:2312.17535v2 Announce Type: replace
Abstract: 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.
Abstract: 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.
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