Papers with Chain-of-Repair
INTERVENOR: Prompting the Coding Ability of Large Language Models with the Interactive Chain of Repair (2024.findings-acl)
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| Challenge: | Experimental results show that INTERVENOR surpasses baseline models, exhibiting improvements of approximately 18% and 4.3% over GPT-3.5 in code generation and code translation tasks. |
| Approach: | They propose a system that prompts Large Language Models to play distinct roles during the code repair process, functioning as both a Code Learner and a code teacher. |
| Outcome: | The proposed system surpasses baseline models in code generation and code translation tasks and improves on syntax errors and assertion errors. |