@inproceedings{zhang-etal-2026-eyemulator,
title = "{E}ye{M}ulator: Improving Code Language Models by Mimicking Human Visual Attention",
author = "Zhang, Yifan and
Huang, Chen and
Zhang, Yueke and
Zhang, Jiahao and
Li, Toby Jia-Jun and
McMillan, Collin and
Leach, Kevin and
Huang, Yu",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.1158/",
pages = "25259--25272",
ISBN = "979-8-89176-390-6",
abstract = "Code Language Models (CodeLLMs) traditionally learn attention based solely on statistical input-output token correlations ({''}machine attention''). In contrast, human developers rely on intuition, selectively fixating on semantically salient tokens during program comprehension. We present EyeMulator, a model-agnostic technique to align CodeLLM attention with human visual attention without architectural changes. By extracting scan paths from eye-tracking data, we derive token-level attention weights used to augment the loss function during fine-tuning. This induces the model to mimic human focus. Our evaluation across StarCoder, Llama-3.2, and DeepSeek-Coder shows that EyeMulator significantly outperforms baselines, achieving gains of over 30 CodeBLEU points in translation and up to 22 BERTScore points in summarization. Ablation studies confirm that these gains stem directly from replicating human attention dynamics. Artifacts are available at https://zenodo.org/records/17205682."
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<abstract>Code Language Models (CodeLLMs) traditionally learn attention based solely on statistical input-output token correlations (”machine attention”). In contrast, human developers rely on intuition, selectively fixating on semantically salient tokens during program comprehension. We present EyeMulator, a model-agnostic technique to align CodeLLM attention with human visual attention without architectural changes. By extracting scan paths from eye-tracking data, we derive token-level attention weights used to augment the loss function during fine-tuning. This induces the model to mimic human focus. Our evaluation across StarCoder, Llama-3.2, and DeepSeek-Coder shows that EyeMulator significantly outperforms baselines, achieving gains of over 30 CodeBLEU points in translation and up to 22 BERTScore points in summarization. Ablation studies confirm that these gains stem directly from replicating human attention dynamics. Artifacts are available at https://zenodo.org/records/17205682.</abstract>
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%0 Conference Proceedings
%T EyeMulator: Improving Code Language Models by Mimicking Human Visual Attention
%A Zhang, Yifan
%A Huang, Chen
%A Zhang, Yueke
%A Zhang, Jiahao
%A Li, Toby Jia-Jun
%A McMillan, Collin
%A Leach, Kevin
%A Huang, Yu
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F zhang-etal-2026-eyemulator
%X Code Language Models (CodeLLMs) traditionally learn attention based solely on statistical input-output token correlations (”machine attention”). In contrast, human developers rely on intuition, selectively fixating on semantically salient tokens during program comprehension. We present EyeMulator, a model-agnostic technique to align CodeLLM attention with human visual attention without architectural changes. By extracting scan paths from eye-tracking data, we derive token-level attention weights used to augment the loss function during fine-tuning. This induces the model to mimic human focus. Our evaluation across StarCoder, Llama-3.2, and DeepSeek-Coder shows that EyeMulator significantly outperforms baselines, achieving gains of over 30 CodeBLEU points in translation and up to 22 BERTScore points in summarization. Ablation studies confirm that these gains stem directly from replicating human attention dynamics. Artifacts are available at https://zenodo.org/records/17205682.
%U https://aclanthology.org/2026.acl-long.1158/
%P 25259-25272
Markdown (Informal)
[EyeMulator: Improving Code Language Models by Mimicking Human Visual Attention](https://aclanthology.org/2026.acl-long.1158/) (Zhang et al., ACL 2026)
ACL
- Yifan Zhang, Chen Huang, Yueke Zhang, Jiahao Zhang, Toby Jia-Jun Li, Collin McMillan, Kevin Leach, and Yu Huang. 2026. EyeMulator: Improving Code Language Models by Mimicking Human Visual Attention. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 25259–25272, San Diego, California, United States. Association for Computational Linguistics.