Does Few-Shot Learning Help LLM Performance in Code Synthesis?

• URL: http://arxiv.org/abs/2412.02906v1
• Date: Tue, 03 Dec 2024 23:19:40 GMT
• Title: Does Few-Shot Learning Help LLM Performance in Code Synthesis?
• Authors: Derek Xu, Tong Xie, Botao Xia, Haoyu Li, Yunsheng Bai, Yizhou Sun, Wei Wang,
• Abstract summary: This work focuses on the few-shot examples present in most code generation prompts. Our work offers 2 approaches for selecting few-shot examples, a model-free method, CODEEXEMPLAR-FREE, and a model-based method, CODEEXEMPLAR-BASED. Both methods significantly improve CodeLlama's coding ability across the popular HumanEval+ coding benchmark.
• Score: 40.35198206199065
• License:
• Abstract: Large language models (LLMs) have made significant strides at code generation through improved model design, training, and chain-of-thought. However, prompt-level optimizations remain an important yet under-explored aspect of LLMs for coding. This work focuses on the few-shot examples present in most code generation prompts, offering a systematic study on whether few-shot examples improve LLM's coding capabilities, which few-shot examples have the largest impact, and how to select impactful examples. Our work offers 2 approaches for selecting few-shot examples, a model-free method, CODEEXEMPLAR-FREE, and a model-based method, CODEEXEMPLAR-BASED. The 2 methods offer a trade-off between improved performance and reliance on training data and interpretability. Both methods significantly improve CodeLlama's coding ability across the popular HumanEval+ coding benchmark. In summary, our work provides valuable insights into how to pick few-shot examples in code generation prompts to improve LLM code generation capabilities.

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