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Selective Prompt Anchoring for Code Generation
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Recent advances in large language models (LLMs) have transformed software development by automatically generating code from natural language. Yet challenges remain in generating fully correct code that aligns with user intent. Our study reveals that LLMs tend to pay less attention to user prompts as more code tokens are generated. We hypothesize that this attention dilution issue is an important reason for code generation errors. To mitigate this issue, we propose Selective Prompt Anchoring (SPA) to guide code LLMs to pay more attention to user intent when generating code. We evaluate SPA using six base LLMs across six benchmarks. Our results demonstrate that SPA enhances Pass@1 by up to 12.9%, consistently outperforming SOTA code generation methods in all settings. Our code is available at https://github.com/magic-YuanTian/Selective-Prompt-Anchoring.
Forward citations
Cited by 2 Pith papers
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Training-Free Token-Level Steering for LLM Personalized Co-Writing
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The Language of Security: How Prompt Syntax Shapes Secure Code Generation in Open LLMs
Removing certain syntactic parts of prompts—especially entity-binding phrases and late-sentence constraints—makes open LLMs generate more vulnerable code, with the strongest effect in Python.
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