通过眼动数据揭示人类对LLM回复的偏好机制，提供新评估视角。
OASST-ETC Dataset: Alignment Signals from Eye-tracking Analysis of LLM Responses
- 基于24人眼动实验，对比优选与非优选回复的阅读模式差异。
- 发现优选回复中人类阅读行为与模型注意力分布相关性更强。
- 适合关注大模型对齐、认知评估及眼动数据应用的研究者。
尽管大型语言模型（LLMs）在自然语言处理中取得显著进展，但使其与人类偏好对齐仍是开放挑战。当前对齐方法主要依赖显式反馈，而眼动（ET）数据可揭示阅读过程中的实时认知加工。本文提出OASST-ETC数据集，记录了24名参与者在评估OASST1数据集生成回复时的阅读模式。分析显示，优选与非优选回复存在显著不同的眼动特征，并与合成眼动数据进行对比。进一步研究发现，人类阅读指标与多种基于Transformer的模型注意力模式在优选回复中具有更强相关性。该工作为研究人类认知加工在LLM评估中的作用提供了独特资源，并为将眼动数据引入对齐方法指明了新方向。数据集与分析代码已公开。
原文摘要 · Abstract (English)
While Large Language Models (LLMs) have significantly advanced natural language processing, aligning them with human preferences remains an open challenge. Although current alignment methods rely primarily on explicit feedback, eye-tracking (ET) data offers insights into real-time cognitive processing during reading. In this paper, we present OASST-ETC, a novel eye-tracking corpus capturing reading patterns from 24 participants, while evaluating LLM-generated responses from the OASST1 dataset. Our analysis reveals distinct reading patterns between preferred and non-preferred responses, which we compare with synthetic eye-tracking data. Furthermore, we examine the correlation between human reading measures and attention patterns from various transformer-based models, discovering stronger correlations in preferred responses. This work introduces a unique resource for studying human cognitive processing in LLM evaluation and suggests promising directions for incorporating eye-tracking data into alignment methods. The dataset and analysis code are publicly available.
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