Summary of How Do Humans Write Code? Large Models Do It the Same Way Too, by Long Li et al.
How Do Humans Write Code? Large Models Do It the Same Way Too
by Long Li, Xuzheng He, Haozhe Wang, Linlin Wang, Liang He
First submitted to arxiv on: 24 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Programming Languages (cs.PL)
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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 Medium Difficulty Summary: This paper proposes Human-Think Language (HTL), a novel approach to improve mathematical reasoning in Large Language Models (LLMs). HTL combines Program-of-Thought (PoT) and Chain-of-Thought (CoT) methods to address the limitations of PoT, which can introduce errors. The authors propose three strategies: a new generation paradigm, Focus Attention, and reinforcement learning. These strategies help integrate CoT and PoT, allowing LLMs to generate more logical code and improve mathematical reasoning accuracy. Experimental results show an average improvement of 6.5% on the Llama-Base model and 4.3% on the Mistral-Base model across 8 mathematical calculation datasets. HTL also demonstrates strong transferability and improves performance in non-mathematical natural language inference tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty Summary: This research paper tries to make computers better at solving math problems. The authors found that a common method, called Program-of-Thought (PoT), sometimes makes mistakes. They created a new way, called Human-Think Language (HTL), which combines two old methods, Chain-of-Thought (CoT) and PoT. HTL helps computers generate more accurate and logical code to solve math problems. The results show that HTL is better than the previous method at solving math problems and even works well on non-math problems. This could lead to more accurate and helpful computer programs in the future. |
Keywords
» Artificial intelligence » Attention » Inference » Llama » Reinforcement learning » Transferability
