Two Tales of Persona in LLM s: A Survey of Role-Playing and Personalization (2024)

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.

## introduction the striking capabilities of large language models (llms), exemplified by chatgpt @xcite , have significantly advanced the field of natural language processing (nlp; @xcite @xcite . recently, in addition to using llms as nlp task solvers or general-purpose chatbots, the question of how to adapt llms for specific context has received great attention. to this end, leveraging personas has resurfaced as an ideal lens for adapting llms in target scenarios @xcite . by incorporating personas, llms can generate more contextually appropriate responses, maximizing their utility and effectiveness for specific applications. however, the growing literature on persona in the llm era is relatively disorganized, lacking a unifying overview. in this paper, we aim to close the gap by offering a comprehensive survey and a systematic categorization of existing studies. specifically, we divide current research into two main streams, namely llm role-playing and llm personalization, as illustrated in figure 1 . the primary distinction is that in role-playing, the persona belongs to the llm, while in personalization, the persona belongs to the user. further, the literature on role-playing mainly focuses on the tasks (i.e., how llms with role-playing can achieve better performance). in contrast, the literature of personalization primarily focuses on the users (i.e., how to satisfy users' expectations and meet their needs). it is noteworthy that both of role-playing and personalization can taxonomy llm personality evaluation ( §4) big five; mbti; etc @xcite ) -dataset -softwaredev @xcite ; srdd @xcite figure 2 : the taxonomy of llm role-playing and llm personalization. to the best of our knowledge, we present the first survey for llm role-playing and llm personalization under the unified view of persona. to foster future endeavors, we actively maintain a paper collection available to the research community. we aim for this work to serve as both a valuable introduction for newcomers and a comprehensive resource for current researchers in the field. our taxonomy is illustrated in figure 2 . we first introduce llm role-playing ( §2), followed by llm personalization ( §3). next, we provide an overview of evaluation methods ( §4) assessing whether the personality of llms (e.g., personality traits or psychological behaviors) accurately aligns with expected personas after the adaptation (i.e., for role-playing llms that act according to assigned personas and personalized llms that fit user personas). lastly, we highlight current challenges and future directions ( §5). we hope that this taxonomy could serve as a useful guideline for researchers to easily target the tasks/scenarios of interests, and swiftly pinpoint their current position in the field. ## llm role-playing llm-based language agents have demonstrated impressive abilities, such as planning, reflection, and tool-use recently @xcite @xcite . the predominant approach of llm role-playing is by coupling personas with language agents, specifically, by incorporating personas directly inside the prompt of language agents. such a training-free paradigm is particularly desirable due to its simplicity and effectiveness. language agents with role-playing elicit the corresponding parametric knowledge in llms to generate responses aligned with assigned personas (i.e., role), enabling them to adapt to various interactive environments. llm role-playing also extends to multi-agent settings, where multiple language agents are equipped with diverse personas, cooperating and communicating with each other to solve complex tasks @xcite . for instance, in one of the first works of role-played llms, @xcite propose generative agents, which engage in a social simulation environment by mimicking human behaviors according to names, ages, and personality traits specified in the prompts. following we introduce different environments and associated roles in which llms adapt to ( §2.1), interactions between llms within the environment ( §2.2), and emergent behaviors stemming from their interactions ( §2.3). figure 3 provides an illustrative overview. ## llm personalization prominent approaches for aligning llms to user intents typically leverage reinforcement learning from human feedback (rlhf), a process that infuses collective consciousness and biases into the model. to enhance individual experience and preference, personalized llms consider user personas (e.g., individual information, historical behaviors) and cater to customized needs @xcite . following we introduce various personalized tasks with associated methods for achieving personalization. figure 4 presents an illustrative overview of personalization tasks. ## llm personality evaluation in the previous sections, we summarize the current progress in llm role-playing and llm person-alization. equally important is the evaluation of whether the personality of llms accurately reflects the intended persona after the adaptation (i.e., for role-playing llms that act based on designated personas and personalized llms tailored to individualized personas). a line of works has carried out the evaluation leveraging human personality assessments, including big five @xcite and mbti @xcite . for example, @xcite ; @xcite quantitatively evaluate llm personality based on the big five personality inventory (bfi) test and story writing test. in the bfi evaluation, llms often can reflect their intended persona accurately. moreover, their personas often influence their linguistic style and personality consistency @xcite . while most works focus solely on either semantic accuracy or personality consistency, @xcite further explore controlling the two aspects simultaneously. @xcite introduce machine personality inventory (mpi) for evaluating llms' personality traits. they use big five personality factors to evaluate each personality trait consisting of a series of descriptions and a set of options and statistically measure each trait. by comparing with human evaluation, they find that the internal consistency correlates with model capabilities. on the other hand, @xcite evaluate llms with the mbti test to assess whether llms possess human-like personalities, and conclude that different llms have different mbti types, which are often attributable to their training corpus. moreover, they find that simply modifying the prompts is unlikely to change the mbti type of llms. another work by @xcite evaluate the personality fidelity of role-playing llms via personality test interviewing, and ask llm to rate the score of each personality dimension according to the interview. their results suggest that llms' demonstrated personalities align well with the assigned character personas. however, whether the aforementioned human psychometric tests are directly transferable to be applied to llms remains an open question @xcite . ## broader implications as llm personalization continues to advance in education domains, individuals could easily access personalized educational contents, lecture materials, and receive affordable tutoring, largely benefiting minority groups with limited resources. however, the concern of polarizing trends might arise, where the privileged group enjoys private tutors and underrepresented individuals only have access to llm-powered supports @xcite . also, personalized llms for healthcare could potentially be widely integrated into clinical scenarios, mental health assessments, or prescribed therapeutic treatments in the near future, where critical questions such as legal considerations of the liability associated with these personalized systems needs careful considerations @xcite . as discussed in ( §4), though methods for llm personality evaluation have been proposed, there still lacks a unifying understanding of how to quantify personality in llms @xcite . @xcite also show that llms sometimes do not hold consistent personalities. it is crucial to continuously explore new measurements for reliable assessment of personality and psychological traits in llms, considering that in the future they might take on more advanced roles and capabilities in society. ## conclusion leveraging personas, llms can generate tailored responses and effectively adapt to a wide range of scenarios. in this survey paper, we summarize two lines of work -role-playing and personalizationfor research of personas in the era of llms. we also present various evaluation methods for llm personality. lastly, we highlight current challenges and promising future directions. we hope our extensive survey and resources serve as an introductory guide for beginners to the field and a practical roadmap to foster future endeavors.