@inproceedings{yehudai-etal-2026-teaching,
title = "Teaching Values to Machines: Simulating Human-Like Behavior in {LLM}s",
author = "Yehudai, Asaf and
Rozen, Naama and
Gera, Ariel",
editor = "Mille, Simon and
Gehrmann, Sebastian and
Schmidtov{\'a}, Patr{\'i}cia and
Du{\v{s}}ek, Ond{\v{r}}ej and
Fadaee, Marzieh and
Lo, Kyle and
Santus, Enrico and
Stanovsky, Gabriel",
booktitle = "Proceedings of the Fifth Workshop on Generation, Evaluation and Metrics ({GEM})",
month = jul,
year = "2026",
address = "San Diego, California, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.gem-main.70/",
pages = "825--847",
ISBN = "979-8-89176-423-1",
abstract = "Large Language Models (LLMs) demonstrate a remarkable capacity to adopt different personas and roles; however, it remains unclear whether they can manifest behavior that adheres to a coherent, human-like value structure. In this work, we draw on established psychological value theory to induce human-like values in LLMs and assess their alignment with patterns observed in human studies.Using validated psychological questionnaires, we conduct large-scale experiments {--} over 5 million questions {--} to evaluate value structures and value{--}behavior relationships in leading LLMs and compare them to humans. Our findings reveal strong agreement between value-prompted LLMs and humans across both dimensions. Moreover, incorporating human value distributions enhances population-level simulations with value-induced LLMs. These findings highlight the potential of value-induced LLMs as effective, psychologically grounded tools for simulating human behavior."
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%0 Conference Proceedings
%T Teaching Values to Machines: Simulating Human-Like Behavior in LLMs
%A Yehudai, Asaf
%A Rozen, Naama
%A Gera, Ariel
%Y Mille, Simon
%Y Gehrmann, Sebastian
%Y Schmidtová, Patrícia
%Y Dušek, Ondřej
%Y Fadaee, Marzieh
%Y Lo, Kyle
%Y Santus, Enrico
%Y Stanovsky, Gabriel
%S Proceedings of the Fifth Workshop on Generation, Evaluation and Metrics (GEM)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-423-1
%F yehudai-etal-2026-teaching
%X Large Language Models (LLMs) demonstrate a remarkable capacity to adopt different personas and roles; however, it remains unclear whether they can manifest behavior that adheres to a coherent, human-like value structure. In this work, we draw on established psychological value theory to induce human-like values in LLMs and assess their alignment with patterns observed in human studies.Using validated psychological questionnaires, we conduct large-scale experiments – over 5 million questions – to evaluate value structures and value–behavior relationships in leading LLMs and compare them to humans. Our findings reveal strong agreement between value-prompted LLMs and humans across both dimensions. Moreover, incorporating human value distributions enhances population-level simulations with value-induced LLMs. These findings highlight the potential of value-induced LLMs as effective, psychologically grounded tools for simulating human behavior.
%U https://aclanthology.org/2026.gem-main.70/
%P 825-847
Markdown (Informal)
[Teaching Values to Machines: Simulating Human-Like Behavior in LLMs](https://aclanthology.org/2026.gem-main.70/) (Yehudai et al., GEM 2026)
ACL