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Two Tales of Persona in LLMs: A Survey of Role-Playing and Personalization

Summary

Work on using personas with large language models has grown quickly but in a scattered way, with no shared classification. This survey by authors from National Taiwan University, Academia Sinica and the University of Virginia organizes the field around a single notion of persona and divides it into two strands. In LLM Role-Playing, the model itself is given a role, such as CTO, doctor or critic, and adapts to an environment. In LLM Personalization, the persona belongs to the user, and the model draws on that person's background or past behaviour to tailor its output.

The authors build a taxonomy that covers:

  • role-playing environments, interaction schemas and emergent behaviours;
  • application domains for personalization;
  • methods for measuring LLM personality.

They also discuss open problems: scarce benchmarks, bias, safety, privacy and broader social effects. As a survey, the paper reports no new experiments. It deliberately skips a cross-paper metric comparison because evaluation practices vary too widely. Its value lies in serving as an entry map for newcomers and a maintained reading list (GitHub: MiuLab/PersonaLLM-Survey).

Key points

  • Role-playing environments: software development (ChatDev, MetaGPT; datasets SoftwareDev, SRDD), games (Generative Agents, Voyager), medicine (DR-CoT, MedAgents) and LLM-as-evaluator (ChatEval, DRPE). Interaction schemas are split into single-agent and multi-agent. Emergent behaviours are grouped as voluntary, conformity and destructive.
  • Personalization domains: recommendation (PALR, BookGPT; MovieLens), search (CoPS), education (EduChat), healthcare (openCHA, HealthLLM; MedDialog, IMCS-21, iCliniq) and dialogue. Dialogue is further divided into ToD modeling (MultiWOZ, SGD) and user persona modeling (PersonaChat, ConvAI2).
  • Personality evaluation: this draws on psychological instruments such as Big Five, MBTI and PsychoBench. Cited studies find that LLMs do not always keep a consistent personality.
  • Risks: assigning personas can amplify stereotypes and toxicity and can make jailbreaking easier (Gupta et al. 2023; Deshpande et al. 2023). Membership inference attacks can expose personal data stored in personalized models.
  • Bibliographic details: arXiv:2406.01171, version 3 (October 2024).

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: Natural language processing

Cited by

92 papers of our library cite it (of the 140884 whose bibliographies we read); 3 of them have a page here, 89 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2024.findings-emnlp.969

@inproceedings{tseng2024two,
  title = {{Two Tales of Persona in LLMs: A Survey of Role-Playing and Personalization}},
  author = {Tseng, Yu-Min and Huang, Yu-Chao and Hsiao, Teng-Yun and Chen, Wei-Lin and Huang, Chao-Wei and Meng, Yu and Chen, Yun-Nung},
  booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2024},
  year = {2024},
  doi = {10.18653/v1/2024.findings-emnlp.969},
  url = {https://doi.org/10.18653/v1/2024.findings-emnlp.969}
}

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