Summary of Codejudge: Evaluating Code Generation with Large Language Models, by Weixi Tong et al.
CodeJudge: Evaluating Code Generation with Large Language Models
by Weixi Tong, Tianyi Zhang
First submitted to arxiv on: 3 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); 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 The proposed CodeJudge framework leverages Large Language Models (LLMs) to evaluate the semantic correctness of generated code without relying on test cases. The study investigates various methods for guiding LLMs in performing “slow thinking” and achieving reliable evaluation. Experimental results demonstrate that CodeJudge outperforms existing methods in most settings, even when using a smaller model like Llama-3-8B-Instruct compared to GPT-3.5-based approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Code Judge is a new way to test how well computers can write code. Computers are getting better at writing their own code, but we need a way to check if it’s correct. Code Judge uses special computer programs called Large Language Models (LLMs) to look at the code and make sure it makes sense. The LLMs are trained to think slowly and carefully about the code, which helps them catch mistakes that might not be caught otherwise. In this study, researchers tested different ways of using the LLMs to evaluate code and found that their method, Code Judge, works better than other methods. |
Keywords
» Artificial intelligence » Gpt » Llama
