Enhancing Code LLM Training with Programmer Attention
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915242934534144 |
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| author | Zhang, Yifan Huang, Chen Karas, Zachary Nguyen, Dung Thuy Leach, Kevin Huang, Yu |
| author_facet | Zhang, Yifan Huang, Chen Karas, Zachary Nguyen, Dung Thuy Leach, Kevin Huang, Yu |
| contents | Human attention provides valuable yet underexploited signals for code LLM training, offering a perspective beyond purely machine-driven attention. Despite the complexity and cost of collecting eye-tracking data, there has also been limited progress in systematically using these signals for code LLM training. To address both issues, we propose a cohesive pipeline spanning augmentation and reward-based fine-tuning. Specifically, we introduce (1) an eye-tracking path augmentation method to expand programmer attention datasets, (2) a pattern abstraction step that refines raw fixations into learnable attention motifs, and (3) a reward-guided strategy for integrating these insights directly into a CodeT5 supervised fine-tuning process. Our experiments yield +7.16 in CodeBLEU on the CodeXGlue benchmark for code summarization, underscoring how uniting human and machine attention can boost code intelligence. We hope this work encourages broader exploration of human-centric methods in next-generation AI4SE. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_14936 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Enhancing Code LLM Training with Programmer Attention Zhang, Yifan Huang, Chen Karas, Zachary Nguyen, Dung Thuy Leach, Kevin Huang, Yu Software Engineering Human-Computer Interaction Machine Learning Human attention provides valuable yet underexploited signals for code LLM training, offering a perspective beyond purely machine-driven attention. Despite the complexity and cost of collecting eye-tracking data, there has also been limited progress in systematically using these signals for code LLM training. To address both issues, we propose a cohesive pipeline spanning augmentation and reward-based fine-tuning. Specifically, we introduce (1) an eye-tracking path augmentation method to expand programmer attention datasets, (2) a pattern abstraction step that refines raw fixations into learnable attention motifs, and (3) a reward-guided strategy for integrating these insights directly into a CodeT5 supervised fine-tuning process. Our experiments yield +7.16 in CodeBLEU on the CodeXGlue benchmark for code summarization, underscoring how uniting human and machine attention can boost code intelligence. We hope this work encourages broader exploration of human-centric methods in next-generation AI4SE. |
| title | Enhancing Code LLM Training with Programmer Attention |
| topic | Software Engineering Human-Computer Interaction Machine Learning |
| url | https://arxiv.org/abs/2503.14936 |