标题： 基于大型语言模型和眼动生物标志的阅读理解分析图表示 显示英文标题

标题： Graph Representations for Reading Comprehension Analysis using Large Language Model and Eye-Tracking Biomarker

摘要： 阅读理解是人类认知发展中的基本技能。 随着大型语言模型（LLMs）的发展，需要比较人类和LLMs在不同情境下理解语言的方式，并将这种理解应用于推理、情感解释和信息检索等功能性任务。 我们之前的工作使用了LLMs和人类生物标志物来研究阅读理解过程。 结果表明，由LLMs标记的与推理目标高相关和低相关的单词对应的生物标志物表现出不同的模式，特别是在使用眼动追踪数据验证时。 然而，仅关注单个单词限制了理解的深度，这使得结论有些简单，尽管它们可能具有重要意义。 本研究使用基于LLMs的AI代理将阅读段落中的单词分组为节点和边，形成基于语义意义和问题导向提示的图结构文本表示。 然后我们比较对重要节点和边的眼动固定分布。 我们的研究结果表明，LLMs在图拓扑结构层面的语言理解上表现出高度一致性。 这些结果建立在我们之前的研究基础上，并提供了有效的师生协同学习策略的见解。 显示英文摘要

摘要： Reading comprehension is a fundamental skill in human cognitive development. With the advancement of Large Language Models (LLMs), there is a growing need to compare how humans and LLMs understand language across different contexts and apply this understanding to functional tasks such as inference, emotion interpretation, and information retrieval. Our previous work used LLMs and human biomarkers to study the reading comprehension process. The results showed that the biomarkers corresponding to words with high and low relevance to the inference target, as labeled by the LLMs, exhibited distinct patterns, particularly when validated using eye-tracking data. However, focusing solely on individual words limited the depth of understanding, which made the conclusions somewhat simplistic despite their potential significance. This study used an LLM-based AI agent to group words from a reading passage into nodes and edges, forming a graph-based text representation based on semantic meaning and question-oriented prompts. We then compare the distribution of eye fixations on important nodes and edges. Our findings indicate that LLMs exhibit high consistency in language understanding at the level of graph topological structure. These results build on our previous findings and offer insights into effective human-AI co-learning strategies.