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A Survey of Incorporating Gaze Data into Natural Language Processing Models and Applications

This study presents a survey of research integrating eye-tracking (gaze) data into Language Models (LMs) as a means of cognitively grounding NLP models and applications in human reading behavior. Although contemporary LMs excel at learning statistical patterns from text, they fundamentally lack human-like reading and comprehension capabilities. Incorporating gaze data may offer a window into cognitive processing, yet its impact on LMs remains underexplored. Addressing a persistent bottleneck, namely, the high cost and limited scale of laboratory eye-tracking, we propose a roadmap consisting of three streams of research for advancing this novel research domain: (1) developing cognitive multimodal corpora, (2) leveraging generative models for gaze synthesis to overcome the data bottleneck caused by the high costs of human eye-tracking, and (3) training LMs with gaze-guided attention mechanisms and input augmentation. Furthermore, we illustrate practical applications in readability assessment, educational analytics, and assistive communication, demonstrating how gaze-informed models can enable adaptive technologies. Finally, we critically examine ongoing challenges, including the lack of data standardization, the misalignment between human and machine language processing, and the urgent ethical imperative for privacy-preserving architectures to protect sensitive biometric gaze data, motivating privacy-aware data practices and model designs for scalable deployment.