arXiv cs.CL· Hikaru Asano, Hidezo Suganuma, Ryutaro Mori, Yukino Baba·· 12 小时前AI 评分31
Cross-Centaur：用语言模型跨心理任务建模个体决策
Cross-Centaur: Modeling Individual Decision-Making Across Psychological Tasks with Language Models
AI 导读
研究者构建 Cross-Psych-101 数据集，涵盖 591 名参与者在 12 项 Bandit、Tradeoff、Structure 和 Social 任务中的 261,551 条有效决策，并训练 Cross-Centaur 从目标任务描述与同一参与者的源任务行为预测其目标任务动作。
正文 · 原文
Abstract:Large language models (LLMs) are increasingly used as computational models of human behavior. Recent models such as Centaur have advanced this goal by fine-tuning LLMs on trial-by-trial decisions from psychological experiments. However, existing large-scale behavioral datasets do not link the same participants across different tasks, so these models learn population-level regularities but are not trained to model individual-level regularities across tasks. We introduce Cross-Psych-101, a dataset of 591 participants and 261{,}551 valid decisions across 12 Bandit, Tradeoff, Structure, and Social tasks. We train Cross-Centaur to predict a participant's target-task actions from a target-task description and the source-task description paired with the same participant's source-task behavior. Cross-Centaur outperforms off-the-shelf LLMs and Centaur on held-out participants, both within and across tasks. Probing analyses suggest that its hidden states encode participant-level behavioral tendencies that transfer across tasks. These results highlight the potential of cross-task individual-level data for predicting human decision-making with language models. Cross-Pysch-101 is available at this https URL.
| Comments: | Accepted to EMNLP 2026 (Main Conference, Oral Presentation) |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.23157 [cs.CL] |
| (or arXiv:2610.23157v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.23157 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Hikaru Asano [view email]
[v1]
Fri, 16 Oct 2026 08:55:00 UTC (1,579 KB)
来源：arXiv cs.CL · arxiv.org