Self-Explained Keywords Empower Large Language Models for Code Generation

ICLR 2025 Conference Submission13053 Authors

Keywords: Large Language Model, Code Generation, Prompt Engineering

TL;DR: We introduce SEK, a simple yet effective method that enhances LLMs' code generation by guiding them to extract, explain, and rank key terms from problem statements.

Abstract: Large language models (LLMs) have achieved impressive performance in code generation. Despite the remarkable success, we observed that LLMs often misunderstand or overlook some problem-specific undertrained keywords during code generation, compromising the accuracy of the generated code. After explicitly explaining these undertrained keywords using well-trained terms in the prompt, LLMs are more likely to generate correct code implementation. Inspired by this observation, we propose a novel technique named SEK (Self-Explained Keywords), which empowers an LLM for better code generation by extracting and explaining the key terms in the problem description with the LLM itself. Comprehensive experiments across three benchmarks, i.e., HumanEval(+), MBPP(+), and APPS, with five representative LLMs, show that SEK can significantly improve LLMs in code generation, yielding substantial and consistent gains. For instance, SEK improves the Pass@1 of DeepSeek-Coder-V2-Instruct from 85.4% to 93.3% on the Humaneval benchmark. Further analysis confirms that SEK enables the LLMs to shift their attention from low-frequency keywords to their corresponding high-frequency counterparts.

Primary Area: applications to computer vision, audio, language, and other modalities

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Submission Number: 13053