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arXiv · 2605.07632

Post-training makes large language models less human-like

Marcel Binz·Elif Akata·Abdullah Almaatouq·Mohammed Alsobay·Oleksii Ariasov·Franziska Brändle·David Broska·Jason W. Burton·Nuno Busch·Frederick Callaway·Vanessa Cheung·Brian Christian·Julian Coda-Forno·Can Demircan·Vittoria Dentella·Maria K. Eckstein·Noémi Éltető·Michael Franke·Thomas L. Griffiths·Fritz Günther·Susanne Haridi·Sebastian Hellmann·Stefan Herytash·Linus Hof·Eleanor Holton·Isabelle Hoxha·Zak Hussain·Akshay Jagadish·Elif Kara·Valentin Kriegmair·Evelina Leivada·Li Ji-An·Tobias Ludwig·Maximilian Maier·Marcelo G. Mattar·Marvin Mathony·Alireza Modirshanechi·Robin Na·Mariia Nadverniuk·Antonios Nasioulas·Surabhi S. Nath·Helen Niemeyer·Kate Nussenbaum·Sebastian Olschewski·Thorsten Pachur·Stefano Palminteri·Aliona Petrenco·Camille V. Phaneuf-Hadd·Angelo Pirrone·Manuel Rausch·Laura Raveling·Shashank Reddy·Milena Rmus·Evan M. Russek·Tankred Saanum·Kai Sandbrink·Louis Schiekiera·Johannes A. Schubert·Luca M. Schulze Buschoff·Nishad Singhi·Leah H. Somerville·Mikhail S. Spektor·Xin Sui·Christopher Summerfield·Mirko Thalmann·Anna I. Thoma·Taisiia Tikhomirova·Vuong Truong·Polina Tsvilodub·Konstantinos Voudouris·Kristin Witte·Shuchen Wu·Dirk U. Wulff·Hua-Dong Xiong·Songlin Xu·Lance Ying·Xinyu Zhang·Jian-Qiao Zhu·Eric Schulz
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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.

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Marcel Binz, Elif Akata, Abdullah Almaatouq, Mohammed Alsobay, Oleksii Ariasov, Franziska Brändle, David Broska, Jason W. Burton, Nuno Busch, Frederick Callaway, Vanessa Cheung, Brian Christian, Julian Coda-Forno, Can Demircan, Vittoria Dentella, Maria K. Eckstein, Noémi Éltető, Michael Franke, Thomas L. Griffiths, Fritz Günther, Susanne Haridi, Sebastian Hellmann, Stefan Herytash, Linus Hof, Eleanor Holton, Isabelle Hoxha, Zak Hussain, Akshay Jagadish, Elif Kara, Valentin Kriegmair, Evelina Leivada, Li Ji-An, Tobias Ludwig, Maximilian Maier, Marcelo G. Mattar, Marvin Mathony, Alireza Modirshanechi, Robin Na, Mariia Nadverniuk, Antonios Nasioulas, Surabhi S. Nath, Helen Niemeyer, Kate Nussenbaum, Sebastian Olschewski, Thorsten Pachur, Stefano Palminteri, Aliona Petrenco, Camille V. Phaneuf-Hadd, Angelo Pirrone, Manuel Rausch, Laura Raveling, Shashank Reddy, Milena Rmus, Evan M. Russek, Tankred Saanum, Kai Sandbrink, Louis Schiekiera, Johannes A. Schubert, Luca M. Schulze Buschoff, Nishad Singhi, Leah H. Somerville, Mikhail S. Spektor, Xin Sui, Christopher Summerfield, Mirko Thalmann, Anna I. Thoma, Taisiia Tikhomirova, Vuong Truong, Polina Tsvilodub, Konstantinos Voudouris, Kristin Witte, Shuchen Wu, Dirk U. Wulff, Hua-Dong Xiong, Songlin Xu, Lance Ying, Xinyu Zhang, Jian-Qiao Zhu, Eric Schulz. 2026-05-25. Post-training makes large language models less human-like. https://arxiv.org/abs/2605.07632

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