@inproceedings{asano-etal-2026-cross,
title = "Cross-Centaur: Modeling Individual Decision-Making Across Psychological Tasks with Language Models",
author = "Asano, Hikaru and
Suganuma, Hidezo and
Mori, Ryutaro and
Baba, Yukino",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.2235/",
pages = "50868--50890",
ISBN = "979-8-89176-459-0",
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 https://huggingface.co/datasets/HikaruAsano/cross-psych-101."
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%0 Conference Proceedings
%T Cross-Centaur: Modeling Individual Decision-Making Across Psychological Tasks with Language Models
%A Asano, Hikaru
%A Suganuma, Hidezo
%A Mori, Ryutaro
%A Baba, Yukino
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F asano-etal-2026-cross
%X 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 https://huggingface.co/datasets/HikaruAsano/cross-psych-101.
%U https://aclanthology.org/2026.emnlp-main.2235/
%P 50868-50890
Markdown (Informal)
[Cross-Centaur: Modeling Individual Decision-Making Across Psychological Tasks with Language Models](https://aclanthology.org/2026.emnlp-main.2235/) (Asano et al., EMNLP 2026)
ACL