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This paper proposes Adaptive Criticism Refinement (ACR), a novel method for code generation using large-scale language models (LLMs). This method overcomes the limitations of existing supervised model distillation methods. It improves performance by iteratively improving self-generated code. ACR utilizes LLMs as judges and critics to assess code quality and improves the model itself through critical feedback on low-quality code. The RefineCoder series developed through this approach achieves comparable or superior performance to existing models on various code generation benchmarks with less data.
Takeaways, Limitations
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Takeaways:
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A Novel Approach to Improving the Performance of LLM-Based Code Generation Models (ACR)
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Reduce reliance on teacher model distillation and increase data efficiency.
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Confirming the possibility of continuous performance improvement through iterative improvement of self-generated code.
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Presenting effective evaluation and improvement strategies using LLMs as judges and critics.
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Limitations:
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The effectiveness of ACR may depend on the performance of LLM.
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The effectiveness of ACR may vary depending on the performance of LLM-as-a-Judge and LLM-as-a-Critic.
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Further research is needed to determine the generalizability of the proposed method.
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Since only results for a specific code generation benchmark are presented, generalization to other domains or tasks is needed.
