发表机构
Georgia Institute of Technology（佐治亚理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究探究话语结构等认知科学确定的指代消解影响因素对GPT-2-XL等五个开源权重LLM的作用，发现部分LLM对部分因素具类人敏感性，明确了LLM近似人类指代消解的条件。
AI 中文摘要
指代语是指称其他表达式（称为先行语）的表达式，将二者关联的过程称为指代消解。认知科学已确定影响指代消解速度与成功率的多个因素，包括话语结构、情境模型属性及语义因素。本研究探究这些因素是否也影响五个开源权重大型语言模型（LLM）的指代消解，分别为GPT-2-XL、Llama-3.1-8B、Pythia-12B、Mistral-7B及Mistral-24B。为建模处理难度，采用标准关联假设，将人类阅读时间与模型在指代语处的意外值（surprisal）关联；作为第二项行为指标，比较模型在探测指代语先行语的理解问题上的准确率与人类准确率。结果显示存在选择性认知对齐：部分LLM在指代消解中表现出对话语显著性及基于距离的因素的类人敏感性，但对语义干扰效应的敏感性较弱或缺失。这些发现明确了LLM近似人类指代消解的条件。
英文摘要
Anaphors are expressions that refer to other expressions, called antecedents. The process of connecting the two is called resolution. Cognitive science has identified multiple factors that affect the speed and success of anaphor resolution, including discourse structure, situation-model properties, and semantic factors. Here, we investigate whether these factors also affect anaphor resolution in five Large Language Models (LLMs) with open weights: GPT-2-XL, Llama-3.1-8B, Pythia-12B, Mistral-7B, and Mistral-24B. To model processing difficulty, we adopt the standard linking hypothesis that relates human reading times to model surprisal at the anaphor. As a second behavioral measure, we compare model accuracy to human accuracy on comprehension questions probing the antecedents of anaphors. The results show selective cognitive alignment: some LLMs exhibit human-like sensitivity to discourse prominence and distance-based factors in anaphor resolution, while showing weaker or absent sensitivity to semantic interference effects. These findings delimit the conditions under which LLMs approximate human anaphor resolution.
Comments7 pages, 6 figures, 1 table. Presented at CogSci 2026 and the 2026 Annual Meeting of the Society for Text & Discourse. Code: https://github.com/wristy/anaphor
