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Cited by 148Speech and dialogue systemsTopic ModelingPersona Design and Applications

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

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Tseng, Yu-Min, Huang, Yu-Chao, Hsiao, Teng-Yun, Chen, Wei-Lin, Huang, Chao-Wei, Meng, Yu, Chen, Yun-Nung·2024·Findings of the Association for Computational Linguistics: EMNLP 2024
DOI 10.18653/v1/2024.findings-emnlp.969
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Abstract

The concept of persona, originally adopted in dialogue literature, has re-surged as a promising framework for tailoring large language models (LLMs) to specific context (e.g., personalized search, LLM-as-a-judge).However, the growing research on leveraging persona in LLMs is relatively disorganized and lacks a systematic taxonomy.To close the gap, we present a comprehensive survey to categorize the current state of the field.We identify two lines of research, namely (1) LLM Role-Playing, where personas are assigned to LLMs, and (2) LLM Personalization, where LLMs take care of user personas.Additionally, we introduce existing methods for LLM personality evaluation.To the best of our knowledge, we present the first survey for role-playing and personalization in LLMs under the unified view of persona.We continuously maintain a paper collection to foster future endeavors.1 * Equal contribution. 1 https://github.com/MiuLab/PersonaLLM-Survey

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Figure 1 In Role-Playing , LLMs act according to assigned personas ( i.e. , roles) under a defined environment. For example, given role names with descriptions , LLMs role-play in a social simulation game. For Personalization , LLMs consider user personas to generate tailored responses to the same question. Dashed rectangles are prompts and solid rectangles are LLMs' responses.
Figure 1 In Role-Playing , LLMs act according to assigned personas ( i.e. , roles) under a defined environment. For example, given role names with descriptions , LLMs role-play in a social simulation game. For Personalization , LLMs consider user personas to generate tailored responses to the same question. Dashed rectangles are prompts and solid rectangles are LLMs' responses.CC-BY · From the PDF
Figure 2 The taxonomy of LLM role-playing and LLM personalization.
Figure 2 The taxonomy of LLM role-playing and LLM personalization.CC-BY · From the PDF
Figure 3 An illustration of four LLM role-playing environments: Software Development (§2.1.1), Game (§2.1.2), Medical Application (§2.1.3), and LLM as Evaluators (§2.1.4). For each environment, we provide a simple scenario with a task description ( red-bordered ) and relevant personas ( i.e. , roles; blue-bordered ). The dashed rectangle represents an example LLM role-playing prompt template. In addition to the above environments, past research also proposes general frameworks applicable to different environments (§5.1).
Figure 3 An illustration of four LLM role-playing environments: Software Development (§2.1.1), Game (§2.1.2), Medical Application (§2.1.3), and LLM as Evaluators (§2.1.4). For each environment, we provide a simple scenario with a task description ( red-bordered ) and relevant personas ( i.e. , roles; blue-bordered ). The dashed rectangle represents an example LLM role-playing prompt template. In addition to the above environments, past research also proposes general frameworks applicable to different environments (§5.1).CC-BY · From the PDF
Figure 4 An illustration of five types of personalized LLMs: Recommendation (§3.1), Search (§3.2), Education (§3.3), Healthcare (§3.4), and Dialogue (§3.5). On the left side, dashed rectangles are prompts, and solid rectangles are the responses of LLMs. On the right side, we depict multi-turn interactions between LLMs and users.
Figure 4 An illustration of five types of personalized LLMs: Recommendation (§3.1), Search (§3.2), Education (§3.3), Healthcare (§3.4), and Dialogue (§3.5). On the left side, dashed rectangles are prompts, and solid rectangles are the responses of LLMs. On the right side, we depict multi-turn interactions between LLMs and users.CC-BY · From the PDF

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Research works printed but not linked: 17

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Metadata

Authors
Tseng, Y.-M., Huang, Y.-C., Hsiao, T.-Y., Chen, W.-L., Huang, C.-W., Meng, Y., & Chen, Y.-N.
Affiliations
National Taiwan University (TW)
ORCID
Venue
Findings of the Association for Computational Linguistics: EMNLP 2024
Publisher
Association for Computational Linguistics
Published
2024
Pages
16612-16631
DOI
10.18653/v1/2024.findings-emnlp.969
OpenAlex
W4404781205
Cited by
87
References
0
Citations by year
2026: 47 · 2025: 35 · 2024: 5
FWCI
25.12
Type
conference paper
Language
en
Open access
Licence
cc-by
Locations
  • Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) (publishedVersion, cc-by)
  • arXiv (Cornell University) (submittedVersion, cc-by-sa)
  • arXiv (Cornell University)
Funders
  • National Taiwan University (113L900902, 113L900903)
  • National Science and Technology Council
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
  • Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
  • Physical Sciences › Computer Science › Human-Computer Interaction › Persona Design and Applications
Keywords
personas, large language models, LLM personalization, personality assessment, personalized search, LLM-as-a-judge

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