GazeSummary: Exploring Gaze as an Implicit Prompt for Personalization in Text-based LLM Tasks
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866914277845106688 |
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| author | Ding, Jiexin Zhang, Yizhuo Liu, Xinyun chen, Ke Wang, Yuntao Patel, Shwetak Gadre, Akshay |
| author_facet | Ding, Jiexin Zhang, Yizhuo Liu, Xinyun chen, Ke Wang, Yuntao Patel, Shwetak Gadre, Akshay |
| contents | Smart glasses are accelerating progress toward more seamless and personalized LLM-based assistance by integrating multimodal inputs. Yet, these inputs rely on obtrusive explicit prompts. The advent of gaze tracking on smart devices offers a unique opportunity to extract implicit user intent for personalization. This paper investigates whether LLMs can interpret user gaze for text-based tasks. We evaluate different gaze representations for personalization and validate their effectiveness in realistic reading tasks. Results show that LLMs can leverage gaze to generate high-quality personalized summaries and support users in downstream tasks, highlighting the feasibility and value of gaze-driven personalization for future mobile and wearable LLM applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_17676 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | GazeSummary: Exploring Gaze as an Implicit Prompt for Personalization in Text-based LLM Tasks Ding, Jiexin Zhang, Yizhuo Liu, Xinyun chen, Ke Wang, Yuntao Patel, Shwetak Gadre, Akshay Human-Computer Interaction Smart glasses are accelerating progress toward more seamless and personalized LLM-based assistance by integrating multimodal inputs. Yet, these inputs rely on obtrusive explicit prompts. The advent of gaze tracking on smart devices offers a unique opportunity to extract implicit user intent for personalization. This paper investigates whether LLMs can interpret user gaze for text-based tasks. We evaluate different gaze representations for personalization and validate their effectiveness in realistic reading tasks. Results show that LLMs can leverage gaze to generate high-quality personalized summaries and support users in downstream tasks, highlighting the feasibility and value of gaze-driven personalization for future mobile and wearable LLM applications. |
| title | GazeSummary: Exploring Gaze as an Implicit Prompt for Personalization in Text-based LLM Tasks |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2601.17676 |