Summary of Chain Of Preference Optimization: Improving Chain-of-thought Reasoning in Llms, by Xuan Zhang et al.
Chain of Preference Optimization: Improving Chain-of-Thought Reasoning in LLMs
by Xuan Zhang, Chao Du, Tianyu Pang, Qian Liu, Wei Gao, Min Lin
First submitted to arxiv on: 13 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this research paper, the authors introduce Chain of Preference Optimization (CPO), a fine-tuning method for large language models (LLMs) that leverages the tree-of-thought (ToT) method to achieve better performance in solving complex problems. CPO is designed to align each step of the chain-of-thought (CoT) reasoning paths with those of ToT using preference information from the tree-search process. The authors demonstrate the effectiveness of CPO by applying it to various tasks, including question answering, fact verification, and arithmetic reasoning, achieving significant improvements in LLM performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models can generate logical reasoning paths for complex problem-solving thanks to chain-of-thought (CoT) decoding. However, these paths are not always deliberate or optimal. The tree-of-thought (ToT) method is designed to find better reasoning paths by exploring the reasoning space. But this deliberation comes at a cost: increased inference complexity. This research shows that fine-tuning LLMs using ToT can help CoT achieve similar results without the extra processing required. |
Keywords
» Artificial intelligence » Fine tuning » Inference » Optimization » Question answering
