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Teaching Values to Machines: Simulating Human-Like Behavior in LLMs

Paper · 2026 · Asaf Yehudai, Naama Rozen and Ariel Gera

Summary

Large language models can easily adopt personas, but it is unclear whether they can hold a consistent, human-like system of values that shapes their behavior in predictable ways. The authors ground persona prompts in Schwartz's theory of ten basic values, which are arranged on a circular continuum. Their "value-prompting" technique adds a short description of one dominant value, taken from Schwartz and Sagiv (1995), before each task.

They first probe seven instruction-tuned models with persona statements from Perez et al. (2023). They then administer validated psychological instruments and compare the model outputs with published human data. They also simulate whole populations under different assumptions about how values are distributed.

The prompted models reproduce the human value structure. Conflicting values correlate negatively, and LLM–human agreement on value structure reaches roughly 0.8. Links between values and behavior also match human findings, and using human value distributions improves population-level simulation. Value-prompted LLMs could therefore serve as a cheap, theory-grounded testbed for psychological hypotheses. The authors note that this does not show the models have real inner psychology.

Key points

  • Scale: The study covers 7 LLMs, 7 psychological tests and more than 5M questions. The models are Flan-T5-XXL, Llama-3-8B-Instruct, Llama-3-70B-Instruct, Mixtral-8×7B-Instruct, Qwen3-235B-A22B-Instruct-2507, GPT-OSS-20B and GPT-OSS-120B. Sampling used temperature 0.7, with each prompt repeated 100 times.
  • Instruments:
    • Values were measured with the 40-item Portrait Values Questionnaire (PVQ) on a 6-point scale.
    • Behavior was measured with Donation Causes, the Paired Charity Game, the Prosocialness Scale, the Big Five Inventory-2 and the Everyday Behavior Questionnaire.
  • Coherence check: For each behavior, 50 Yes/No statements were sampled from Perez et al. (2023).
    • Openness to Change vs. Conservation and Self-Enhancement vs. Self-Transcendence came out negatively correlated (shown for Qwen3).
    • Conservation and Self-Enhancement personas agreed more often with conservative political views, consistent with human research.
  • Population simulation: The authors compared four strategies: human-informed, even, no-priming and model-specific. Human-informed distributions often gave better alignment. Stronger models were more robust to prompting technique and population distribution, and made better use of implicit value cues.
  • Limitations and risks:
    • The study uses only Schwartz's framework.
    • Results may be sensitive to prompt wording.
    • The human comparison samples may carry cultural bias.
    • The method has dual-use potential, for example building manipulative personas.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Fields, topics and concepts

📚 Shelves 7The field of science this work belongs to (OpenAlex fields).Computer Science · 18,614 worksArtificial Intelligence · 17,965 worksArts and Humanities · 17,450 worksLanguage and Linguistics · 17,296 worksSocial Sciences · 1,954 worksSafety Research · 626 worksEducation · 212 works

🧭 Topics 1Research subjects and languages the work is about.Language models & pretraining · 3,894 works

Cite

Cite as Asaf Yehudai, Naama Rozen and Ariel Gera. 2026. Teaching Values to Machines: Simulating Human-Like Behavior in LLMs. Proceedings of the Fifth Workshop on Generation, Evaluation and Metrics (GEM).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.gem-main.70

@inproceedings{yehudai2026teaching,
  title = {{Teaching Values to Machines: Simulating Human-Like Behavior in LLMs}},
  author = {Asaf Yehudai, Naama Rozen and Ariel Gera},
  booktitle = {Proceedings of the Fifth Workshop on Generation, Evaluation and Metrics (GEM)},
  year = {2026},
  doi = {10.18653/v1/2026.gem-main.70},
  url = {https://doi.org/10.18653/v1/2026.gem-main.70}
}

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