Modeling, Evaluating, and Embodying Personality in LLM s: A Survey (2025)

Abstract: As large language models (LLMs) become integral to social and interactive applications, the ability to model, control, and evaluate their personality traits has become a critical area of research. This survey provides a comprehensive and structured overview of the LLM-driven personality scenario. We introduce a functional taxonomy that organizes the field by how personality is modeled (from rule-based methods to model-centric and system-level LLM techniques), across which modalities it is expressed (extending beyond text to vision, speech, and immersive virtual reality), and how it is validated (covering both qualitative and quantitative evaluation paradigms). By contextualizing current advances and systematically analyzing the limitations of existing methods including subjectivity, context dependence, limited multimodal integration, and the lack of standardized evaluation protocols, we identify key research gaps. This survey serves as a guide for future inquiry, paving the way for the development LLMs with more consistent consistent, expressive, and trustworthy personality traits.

## introduction recent breakthroughs in large language models (llms) have reshaped human-computer interaction, enabling systems that communicate with a fluency once reserved for human-to-human dialogue. these systems now power chatbots @xcite , code assistants @xcite , and multimodal agents @xcite that emulate rich, real-world communication in purely digital settings. consequently, the research agenda has expanded beyond model scaling to encompass data-efficient training strategies @xcite , rigorous evaluation frameworks for ensure quality and safety @xcite , and investigations about how to emulate human behavior in digital environments @xcite . * corresponding author: iagoalves@discente.ufg.br a subtler frontier within this evolving landscape is the extent to perform consistent and recognizable personality traits that enrich user engagement using llms @xcite . as these models increasingly mediate social, educational, and assistive interactions, their perceived personality plays a critical role in shaping user trust, satisfaction, and long-term adoption @xcite . this emergent focus has sparked growing interest in how personality traits arise in llms, whether through pre-training data, instruction tuning, or prompt design, and how these traits can be measured, controlled, or aligned with user expectations and application goals. furthermore, the study of personality in llms raises fundamental questions at the intersection of artificial intelligence (ai), linguistics and psychology. unlike traditional systems that rely on hardcoded traits, scripted responses, or purely statistical methods such as n-gram text generation @xcite , llms can dynamically adapt their tone and style based on subtle contextual cues, achieving stable personality profiles and manifesting distinct persona-like behaviours when confronted with the same question or, conversely, sustain a coherent persona across disparate tasks, as illustrated in figure 1 . this has led to the development of new methodologies for personality assessment, drawing from established psycholin-guistic frameworks such as the big five inventory @xcite , as well as the creation of novel evaluation protocols tailored to generative ai systems @xcite . additionally, since personality expression extends beyond text, integrating multimodal signals (i.e. voice tone, facial expressions, and gesture) remains a key challenge, calling for multidisciplinary approaches that bridge language, vision, and speech technologies. in light of these developments, this survey provides a comprehensive overview of the emerging landscape of personality in llms. while prior surveys provide valuable syntheses of role-playing agents and personalization strategies @xcite , our work offers a broader and complementary contribution, extending the discussion beyond persona assignment to personality as a psychological trait. we also provide a more comprehensive methodological coverage, encompassing pre-llm approaches, model-centric, and system-level approaches, and multimodal perspectives across language, vision, audio, and the impact of personality-based agents in virtual reality (vr) environments. moreover, our survey systematically analyzes evaluation methodologies, distinguishing qualitative and quantitative evaluation paradigms and critically comparing their strengths and limitations. by tracing the field's trajectory from early methods to generative models, we contextualize current advances, identify key research gaps, and outline promising directions for future works. together, these dimensions position our survey as a distinct and complementary reference point in the literature, bridging psychological grounding with nlp practice and offering the first systematic account of multimodal personality modeling. in the following sections, we delve into a discussion of related studies, organized according to our proposed taxonomy (summarized in figure 2 ). this taxonomy provides a functional decomposition of the personality modeling process. we begin by covering how personality is modeled, from early approaches to contemporary llm-driven methods. we then explore personality across vision and speech modalities and how embodied personality shapes interaction within virtual reality environments. next, we examine how personality is validated, covering both qualitative and quantitative evaluation methods. finally, we position challenges and future directions, as well as address an ethical discussion about personality modeling. this structure allows us to highlight the field's chal-lenges and potential gaps, paving paths for further research personality modeling. ## early approaches to personality modeling early studies focused on the identification of personality traits and stylistic patterns through rulebased systems with manually curated lexical resources @xcite , as well as handengineered features such as word counts and ngrams @xcite . some approaches also leveraged distributional semantics and classical embeddings, combined with traditional machine learning algorithms @xcite . however, these methods were constrained by the limited expressiveness of their representations and a lack of contextual understanding. consequently, most studies remained focused on classification tasks, rarely addressing the dynamic and generative aspects of personality expression in dialogue. more recently, the usage of pretrained language models (plms) was applied to personality modelling task. @xcite explored the usage of bert @xcite and gpt2 @xcite to both classify and generate human personality texts. their study demonstrated that plms could be steered toward producing outputs consistent with specific personality traits, highlighting the potential of generative models for simulating psychological dimensions. however, advances since then in model scale, training paradigms, and system design have substantially broadened what is possible for personality modeling. while valuable as an initial step toward automatic personality generation, these efforts did not yet capture the emergent, dynamic, and multimodal aspects of personality expression that are increasingly salient in the era of large-scale generative models. ## llm-driven personality the introduction of the transformer architecture @xcite marked a paradigm shift in natural language processing, giving rise to decoderbased models capable of generating fluent and coherent text at scale. these models leverage massive datasets spanning diverse domains to learn rich representations of language, enabling generalization across a broad range of tasks @xcite . ## personality modeling beyond text although textual dialogue allows to convey many aspects of personality, finer-grained affective cues, such as intonation, facial micro-expressions, gesture, and the environment, emerge only when additional modalities are brought into the loop. embedding llms within speech, vision, and engaging interfaces therefore enriches the communicative channel, supplying a denser signal space from which stable and nuanced personality displays can arise. ## evaluating llms personality traits the psychology of personality has long sought to classify individual differences @xcite , and the tight coupling between language and personality @xcite ) makes text an appealing lens for probing llm behaviour. recent studies test trait stability @xcite , refine measurement protocols @xcite , analyse safety implications @xcite , and tailor personas to task requirements @xcite , yet nearly all rely on frameworks devised for humans @xcite . consequently, current approaches to evaluating personality in llms are fragmented,shaped by subjective interpretations and context-dependent outcomes, and, most critically, lack standardized benchmarks. this absence of common evaluation protocols prevents consistent comparison across studies and hinders the development of cumulative, reproducible insights. the following sections review qualitative and quantitative approaches, highlighting their advantages, drawbacks, and suitability for conversational agents. ## challenges and future directions despite recent advances, personality modeling with large language models remains limited by several unresolved challenges. prompt-based techniques, while flexible, are inherently fragile and prone to producing inconsistent outputs across tasks and domains. supervised fine-tuning, though more stable, remains constrained by data scarcity, high computational cost, and risks of overfitting or catastrophic forgetting. these limitations are further exacerbated in multi-agent systems, where inconsistent persona enactment can disrupt coordination, leading to degraded performance in collaborative settings. although personality expression is inherently multimodal, encompassing prosody, facial expression, and gesture, current approaches rarely integrate other modalities. this restricts the validity of simulated personalities, particularly in digital humans and vr environments. additionally, the lack of standardized, robust evaluation protocols remains as a barrier. current assessment strategies exhibit high sensitivity to prompt phrasing, task framing, and input order, undermining reproducibility and comparability across studies. moreover, existing methods often assume stable, human-like personality structures, which may not align with the dynamic and contextdependent nature of llm behavior. to advance the field, several directions requires further exploration. first, scalable personalization techniques, such as parameter-efficient fine-tuning, offer promising paths for adapting traits across users and applications. second, integrating multimodal capabilities, including speech synthesis and visual embodiment, may enable more realistic and expressive personality representations. third, the development of prompt-invariant, context-aware, and psychometrically grounded evaluation benchmarks is essential to establish methodological rigor. finally, personality-aware alignment frameworks must ensure that trait-driven behaviors remain safe, coherent, and socially appropriate 7 ethical discussion personality modeling in llms introduces unique ethical challenges that warrant dedicated attention. persuasive or emotionally adaptive personas risk enabling manipulative behavior, while trait-based modeling grounded in frameworks such as the big five can perpetuate stereotyping. identity simulation further raises concerns about impersonation of public figures or fictional characters, blurring boundaries between authenticity and fabrication. moreover, long-term adaptive interactions highlight issues of consent and transparency, as users may be unaware of how personas evolve over time. addressing these risks requires deliberate design from in data collection to personality modeling strategies to ensure that research advances responsibly and avoids unintended consequences. ## conclusion in this paper, we present a comprehensive survey of personality modeling in large language models, covering foundational methods, llm-driven techniques, multimodal approaches, and evaluation strategies. we analyze how personality traits are identified, induced, and evaluated, and we categorize the current landscape into a structured taxonomy. to the best of our knowledge, this is the first survey to synthesize the diverse field of personality modeling, bridging core llm advancements such as agents and rag with cutting-edge multimodal research in virtual reality. we aim to consolidate the state of the art, identify open challenges, and offer insights to guide future research in building consistent, expressive, and user-aligned llms.