一句话：提出眼动数据增强、注意力模式抽象与奖励引导微调的流水线，用人类注意力信号提升代码LLM训练效果。
该论文关注人类注意力信号在代码大语言模型训练中未被充分利用的问题：眼动数据采集复杂昂贵，且缺乏系统性利用这些信号的方法。作者提出一条涵盖数据增强与奖励微调的连贯流水线，具体包括：眼动路径增强方法以扩充程序员注意力数据集；模式抽象步骤将原始注视点提炼为可学习的注意力motif；以及奖励引导策略，将这些洞见直接融入CodeT5的监督微调过程。实验在CodeXGlue代码摘要基准上取得CodeBLEU +7.16的提升，表明融合人类与机器注意力能够增强代码智能，并鼓励在下一代AI4SE中探索以人为中心的方法。
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.
@inproceedings{paperbot9154,
title = {Enhancing Code LLM Training with Programmer Attention},
author = {Yifan Zhang and Chen Huang and Zachary Karas and Thuy Dung Nguyen and Kevin Leach and Yu Huang},
booktitle = {FSE},
year = {2025},
doi = {10.1145/3696630.3728510}
}