Summary of Top Pass: Improve Code Generation by Pass@k-maximized Code Ranking, By Zhi-cun Lyu et al.
Top Pass: Improve Code Generation by Pass@k-Maximized Code Ranking
by Zhi-Cun Lyu, Xin-Ye Li, Zheng Xie, Ming Li
First submitted to arxiv on: 11 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Software Engineering (cs.SE)
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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 This paper proposes Top Pass, a novel approach for identifying potential correct solutions from a large number of code candidates generated by Large Language Models (LLMs). The current LLM-based code generation approaches struggle to generate error-free code in a few tries when faced with complex problems. To address this issue, the prevailing strategy is to sample a huge number of candidate programs, hoping that any one of them could work. However, users expect to find a correct program by reviewing or testing only a small number of code candidates. Otherwise, the system would be unhelpful. Top Pass directly optimizes the pass@k loss function, enhancing the quality at the top of the candidate list. This enables the user to find the correct solution within as few tries as possible. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it easier for computers to generate code that works correctly. Right now, this process is like trying to find a needle in a haystack. The computer generates many different code options and then hopes one of them will work. But what if we could make the computer prioritize the best options? That’s exactly what Top Pass does. It takes all those code options and ranks them so that the correct solution is at the top. This makes it much easier for people to find the right code. The paper shows that this approach works well, especially on complex problems. |
Keywords
» Artificial intelligence » Loss function
