Post-training makes large language models less human-like
Abstract
Large language models (LLMs) are increasingly used as surrogates for human participants, but it remains unclear which models best capture human behavior and why. To address this, we introduce Psych-201, a novel dataset that enables us to measure behavioral alignment at scale. We find that post-training -- the stage that turns base models into useful assistants -- consistently reduces alignment with human behavior across model families, sizes, and objectives. Moreover, this misalignment widens in newer model generations even as base models continue to improve. Finally, we find that persona-induction -- a popular technique for eliciting human-like behavior by conditioning models on participant-specific information -- does not improve predictions at the level of individuals. Taken together, our results suggest that the very processes that are currently employed to turn LLMs into useful assistants also make them less accurate models of human behavior.
Cite
@article{arxiv.2605.07632,
title = {Post-training makes large language models less human-like},
author = {Marcel Binz and Elif Akata and Abdullah Almaatouq and Mohammed Alsobay and Oleksii Ariasov and Franziska Brändle and David Broska and Jason W. Burton and Nuno Busch and Frederick Callaway and Vanessa Cheung and Brian Christian and Julian Coda-Forno and Can Demircan and Vittoria Dentella and Maria K. Eckstein and Noémi Éltető and Michael Franke and Thomas L. Griffiths and Fritz Günther and Susanne Haridi and Sebastian Hellmann and Stefan Herytash and Linus Hof and Eleanor Holton and Isabelle Hoxha and Zak Hussain and Akshay Jagadish and Elif Kara and Valentin Kriegmair and Evelina Leivada and Li Ji-An and Tobias Ludwig and Maximilian Maier and Marcelo G. Mattar and Marvin Mathony and Alireza Modirshanechi and Robin Na and Mariia Nadverniuk and Antonios Nasioulas and Surabhi S. Nath and Helen Niemeyer and Kate Nussenbaum and Sebastian Olschewski and Thorsten Pachur and Stefano Palminteri and Aliona Petrenco and Camille V. Phaneuf-Hadd and Angelo Pirrone and Manuel Rausch and Laura Raveling and Shashank Reddy and Milena Rmus and Evan M. Russek and Tankred Saanum and Kai Sandbrink and Louis Schiekiera and Johannes A. Schubert and Luca M. Schulze Buschoff and Nishad Singhi and Leah H. Somerville and Mikhail S. Spektor and Xin Sui and Christopher Summerfield and Mirko Thalmann and Anna I. Thoma and Taisiia Tikhomirova and Vuong Truong and Polina Tsvilodub and Konstantinos Voudouris and Kristin Witte and Shuchen Wu and Dirk U. Wulff and Hua-Dong Xiong and Songlin Xu and Lance Ying and Xinyu Zhang and Jian-Qiao Zhu and Eric Schulz},
journal= {arXiv preprint arXiv:2605.07632},
year = {2026}
}