Selective Prompt Anchoring for Code Generation

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 S *elective P rompt A* nchoring (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.

Authors

• Yuan Tian
• Tianyi Zhang 0001

Keywords

• Large Language Models (LLMs)
• Prompt
• Code Generation
• Self-attention
• Taylor expansion
• Logits
• Anchoring