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Synthetic Socratic Debates: Examining Persona Effects on Moral Decision and Persuasion Dynamics

Paper · 2025 · Jiarui Liu, Yueqi Song, Yunze Xiao et al.

Summary

LLMs are often given personas and asked for advice on moral questions, yet earlier studies usually varied a single trait or relied on artificial dilemmas. This paper examines how combinations of identity traits shape an LLM's moral verdicts and its persuasion style. The authors sampled 131 highly contested, relationship-centered dilemmas from the SCRUPLES ANECDOTES corpus, which originates from Reddit's AITA forum, and framed each as a question of whether the author or the other party is to blame. Each agent's persona combined six traits: age, gender, and country, along with its ideological leaning, its class background, and a Big Five personality profile. First, each agent gave a single-turn stance. Then pairs of agents that disagreed debated for several turns while a moderator tracked convergence. Arguments were coded with Aristotle's ethos, pathos, and logos. Ideology and personality turned out to be the strongest drivers of both initial stances and debate outcomes, with liberal and high-openness personas reaching consensus more readily. These findings argue for evaluating AI moral reasoning with personas in mind.

Key points

  • Models tested: GPT-4o, Claude-3.5-Sonnet, LLaMA-4-Maverick, and Qwen3-235B-A22B. The Limitations section, however, says the analysis centers mainly on GPT-4o and LLaMA-4-Maverick.
  • Personas: 500 were sampled from roughly 10,000 possible combinations of the six traits. All prompts and debates were in English.
  • Debate protocol: two agents alternate for at most 5 turns. After each turn, they update a 5-point moral stance rating. A moderator stops the debate once ratings converge or the turn limit is hit.
  • Evaluation metrics include self-alignment, consensus formation, debate efficiency, and logit-based confidence. Confidence tends to increase over the course of a debate, while emotional (pathos) and credibility (ethos) appeals weaken.
  • Released resources: the dataset is on Hugging Face (Jerry999/SyntheticSocraticDebates) and the code on GitHub. API usage cost about $1000.

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

 Institutions

🎓Carnegie Mellon UniversityPittsburgh · United States539 works · 179,533 citations🎓Duke UniversityDurham · United States28 works · 15,077 citations

 Fields, topics and concepts

📚 Shelves 8The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksSocial Sciences · 1,772 worksSafety Research · 543 worksNeuroscience · 165 worksCognitive Neuroscience · 164 works

Cite

Cite as Jiarui Liu, Yueqi Song, Yunze Xiao, Mingqian Zheng, Lindia Tjuatja, Jana Schaich Borg, Mona T. Diab and Maarten Sap. 2025. Synthetic Socratic Debates: Examining Persona Effects on Moral Decision and Persuasion Dynamics. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2025.emnlp-main.831

@inproceedings{liu2025synthetic,
  title = {{Synthetic Socratic Debates: Examining Persona Effects on Moral Decision and Persuasion Dynamics}},
  author = {Jiarui Liu, Yueqi Song, Yunze Xiao, Mingqian Zheng, Lindia Tjuatja, Jana Schaich Borg, Mona T. Diab and Maarten Sap},
  booktitle = {Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing},
  year = {2025},
  doi = {10.18653/v1/2025.emnlp-main.831},
  url = {https://doi.org/10.18653/v1/2025.emnlp-main.831}
}

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