Olapa-MCoT: Enhancing the Chinese Mathematical Reasoning Capability of LLMs
Quick summary
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
Key takeaways
- 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?
Why it matters
This model development creates a new option for users and a new testing obligation for developers. A fixed evaluation set comparing quality, cost and failure behavior is more useful than launch claims.
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