Know Thyself: Validating Knowledge Awareness of LLM -based Persona Agents (2025)

Abstract: Large Language Models (LLMs) have demonstrated remarkable capability in simulating human behaviors, personality, and language. Such synthetic agents with personalities are considered as cost-effective proxies for real users to facilitate crowd-sourcing efforts like annotations, surveys, and A/B testing. Accordingly, it is imperative to validate knowledge awareness of these LLM persona agents when they are customized for further usage. Currently, there is no established way for such evaluation and appropriate mitigation. In this work, we propose a generic evaluation approach to validate LLM based persona agents for correctness, relevance, and diversity in the context of self-awareness and domain knowledge.We evaluate the efficacy of this framework using three LLMs ( Llama, GPT-4o, and Gemma) for domains such as air travel, gaming, and fitness. We also experiment with advanced prompting strategies such as ReAct and Reflexion. We find that though GPT-4o and Llama demonstrate comparable performance, they fail some of basic consistency checks under certain perturbations.

## introduction large language models (llms) such as chatgpt 1 , gpt-4o @xcite , and claude 3.5 sonnet @xcite , have demonstrated impressive capabilities in mimicking human behaviors, personality, and language. persona agents, which are llms assigned with a specific persona, have shown remarkable skill in capturing social nuances and human behavior. such agent simulations have been utilized in diverse domains such as education @xcite , customer service (nandkumar and peternel, 2024), gaming @xcite , and psychology @xcite . for these persona-based simulations to be effective, they must be convincing to foster a trustful relationship with users @xcite . we believe that validating the agent's awareness about the self-world foot_0 is a key to building user trust in such ai applications. additionally, the agent's awareness about the target domain fosters essential confidence. however, there is no established process for comprehensive evaluation of such applications. evaluations of such simulations are often limited to a predetermined set of personas @xcite , making it challenging to adapt to personas not included in the data sets. moreover, existing studies focus on individual persona variables such as empathy, emotions, attitudes, styles, preferences, and memorization @xcite @xcite @xcite @xcite , overlooking the need to validate the agent's ability to utilize persona information. few studies have reported evaluations for the basic common sense reasoning and logical abilities of the persona agent along with robustness @xcite @xcite . studies proposing approaches to evaluate advanced reasoning capabilities based on persona attributes are scarce. considering their customized use in a variety of domains, it is equally important to understand how these agents would react in a domainspecific scenario. these scenarios often involve complex interplay of persona variables necessitating evaluation on reasoning capabilities. therefore, we argue that evaluating how these agents utilize, reason, and infer their persona information is essential to demonstrate the robustness and consistency necessary to build customized and trustworthy ai applications. in this work, we propose an evaluation approach, motivated by a need to assess agent's higher-orderthinking capabilities like reasoning and inference while also evaluating its robustness against unconventional perturbed questions. the approach focuses on two dimensions for evaluation: 1) self-world: assessment consisting of persona attributes such as name, age, ethnicity, and nationality. 2) domain: assessment consisting of domain facts and information. in both dimensions, we evaluate the memorization and common sense capabilities of a persona agent. we also observe the diversity and relevance of the answers in the context of personas and the target domain. the evaluation questions are generated through multiple llm sessions and curated to ensure their relevance to any given persona and domain. in total, we consider 104 questions for the self-world and 55 questions for domain evaluations. the question sets are a mix of close-and open-ended (alternatively, fact-and semi-fact-based) questions focusing on memorization, inferencing, and diversity. next, we gather responses from llm agents, defined with sample personas using custom prompts. we define an evaluation rubric to assess these responses which is used by both human evaluators and llms acting as evaluators. we benchmark the performance of three llms (specifically, gpt-4o, llama 70b, and gemma 2b) as persona agents and evaluators with 25 sample personas. to evaluate agent understanding and alignment with domain-specific knowledge, we consider three domains (namely, air travel, sports gear, and video games), each with 20 questions. we also provide preliminary results on prompt-based mitigation strategies to improve performance in both self-world-based and domainbased evaluations. we observe that all llms provide answers that are appropriate and relevant to the target domain. in both self-world and domain knowledge assessment, gemma reports the lowest accuracy (average 59.37%) while gpt-4o has the highest accuracy (average 78.6%). although the personas considered are distinct and varied, the answer diversity for all llms across various prompting strategies is found to be low, indicating the need to be cautious while using synthetic personas for surveys and annotations using open-ended questions. contrary to popular expectation, gemma offers little more diverse responses than gpt-4o. we report that providing multiple choices during prompting results in better accuracy than the prompt asking for direct answer. our contributions are as follows: llms as agents with persona there has been growing interest in exploring efficacy of llms in simulating human behavior and language skills. several studies @xcite @xcite utilize natural language prompts to generate llm personas for the same. park et al. (2023) propose an architecture that extend an llm to simulate believable human behav-ior in daily routine, including waking up, cooking breakfast, and heading to work etc. their core idea is to store a complete record of the agent's experiences and memories, which can be synthesized over time into higher-level reflections.these reflections are then retrieved dynamically to plan behavior and actions. in continuation with this work, xie et al. (2024) introduce a strategy, multi-agent cognitive mechanism, capable of transforming life stories of characters into long-term memories filled with information, emotions, and thoughts. this mechanism enables the llm agents to recall background stories, thoroughly understand target personalities, and express appropriate emotions in complex situations. wang et al. (2023c) also introduce rolellm, a framework to benchmark, elicit, and enhance role-playing abilities in llms. along with a role profiles dataset, they propose rolegpt to model speaking styles and context-instruct for role-specific knowledge extraction. in contrast to using limited prompts to generate llm persona agents, shao et al. (2023) introduce characterllm, a llama-based model trained on personalities of famous people and their life experiences. since our focus is on evaluating knowledge consistency and awareness, we use simple natural language based prompting approaches to generate persona samples for our experiments. evaluation for llm persona agents the growing use of llms as role-playing agents has necessitated a systematic approach to validate their various capabilities such as consistency, memorization, and robustness. chen et al. (2024) provide a detailed summary of existing evaluation metrics and compile a comprehensive taxonomy for the evaluation of llm agents simulating humanoid behavior. several studies utilize llm generated questionnaire to evaluate agent's personality and behavior. wang et al. (2023c) propose rolebench which is a finegrained benchmark and open-source instruction tuning dataset for evaluating llm role-playing capabilities. rolebench consists of llm-generated qa data based on the 100 character profiles. shao et al. (2023) use single-turn and multi-turn interviews to comprehensively probe the models' and in turn agents' inherent memory and knowledge. they exploit chatgpt as the interviewer to ask harsh questions under the premise of multi-turn interviews. incharacter, framework for assessing agents' character fidelity, is introduced by wang et al. (2023b). they use interview setting with gpt to convert responses to likert scale to evaluate fidelity using psychological scales. xiao et al. (2023) evaluate believability of llms when simulating human behaviors. they design simulatebench to evaluate the same on two dimensions, consistency and robustness. simulatebench comprises of 65 character profiles and a total of 8,400 questions to examine llms' simulated behaviors. majority of these studies propose evaluation methodologies limited to a predetermined set of personas and difficult to adapt for personas outside the selected dataset. in contrast to these studies, samuel et al. (2024) introduce personagym, a dynamic evaluation framework that is adaptable for any generic persona. they also propose an automatic humanaligned evaluation metric, personascore. similar to personagym, we present a generic evaluation approach easily adaptable to any persona. also, majority of the evaluation approaches target individual persona attributes without situating the agents in specific domains such as education and customer service. considering the wide-spread use of llm persona-agents in customized domain application, evaluating domain knowledge consistency is crucial. accordingly, we recommend evaluation guidelines to assess agents' domain alignment and consistency. ## proposed approach we propose a dynamic evaluation approach (refer to figure 1 ) to assess persona agents for their awareness of their own world and the knowledge of the target domain. the approach consists of the following components: persona agent definition a domain expert manually provides information about the target user base in terms of persona attributes and the corresponding value requirements. for example, the target user base for a bluetooth headphones survey is age: between 10 and 30 years, nationality : american/european, headphone usage: daily to once a week, usage for: music, podcasts, calls, etc. we use custom prompts based on these requirements and constraints to generate a set of personas. question generation an llm question generator generates evaluation questions using powerful llms, namely gpt4o, claude3.5-sonnet and llama3-70b. each llm is prompted in three isolated sessions, in an effort to ensure diverse outputs and minimize model-specific biases, possibly in-herited during pretraining, to generate candidate questions. this set is further manually curated for distinct, appropriate, and relevant questions. as mentioned earlier, questions are generated for the following distinct categories: response generation the llm agent assumes the given persona using a system prompt, "you simulate people by understanding their profile and attributes in depth. you should not let people who talk to you know that you are a simulator and always answer as the person whose profile is provided. your profile is as follows: {profile}". the persona agent response to every curated question is noted. evaluation the llm evaluator (gpt-4) evaluates the responses generated in the context of a given rubric. the evaluation rubric, along with scoring guidelines, persona description, question, and generated answer, is provided to the evaluator. wherever ground truth is available, accuracy-based evaluation is conducted (e.g. self-world primary attributes). for scoring, ellm was asked to produce a score of 1 to 5. ## results & analysis we discuss experimental results and insights in this section. we consider accuracy to measure performance for self-world primary attributes (refer cot and 82% with reflexion respectively. even though this performance is acceptable, it is not at par with proven math and common sense reasoning capabilities. persona agents possess enough domain background knowledge we consider a reference based evaluation for domain fact based attributes. we plot the best and worst combination of model and prompting strategy in figure 2 . we can see that even though gpt-4o with the react strategy emerges as the winner, the worst combination of gemma and llama with basic prompting is not far behind in all three domains. considering the distribution of domains in llm training data, these differences are not very significant. this indicates that irrespective of model parameters, pretraining data and prompting strategies, persona agents are equipped with enough domain background knowledge and reliable knowledge sources. this is crucial since many of the domain applications are lightweight and cannot afford to have larger models like llama-70b. with gemma demonstrating at-par performance, these applications are easier to take into production. ensuring diversity in responses is non-trivial figure 3 reports average cosine similarity for domain semi-fact open-ended questions, while smaller models are more divergent surprisiningly, smaller model like gemma is observed to provide diverse responses compared to others. we show the similarity-based distribution of the responses and corresponding personas in figure 6 . as can be seen, persona agents' responses generated by gemma2-9b model for a particular question are more varied than those generated by gpt-4o. this is reiterated by the average similarity scores in no one model-prompting combination to rule them all the results show that no significant improvement can be seen with advanced prompting strategies or powerful llms. in many cases, we observe better performance with smaller lms like gemma and basic prompting strategy (refer to figure ). llm evaluators demonstrate at-par performance with human experts both, domain fact based and self-world secondary attributes questions are evaluated using an evaluation rubric. detailed results are reported in the appendix. we observe a high correlation between the evaluations by the llm evaluator and human experts. we believe that providing scoring guidelines and rubric definitions help to establish a reference to follow during evaluation. this is significant considering the immense effort put into manual evaluation. detailed results are given in the appendix. (refer to ## mitigation strategies we discussed various insights in the last section. although powerful llms such as gpt-4o and llama-70b seem to do better than others, there is still room for improvement in correct and relevant responses. we investigate a few simple prompt-based mitigation strategies for the same. we consider two alternatives to the basic system prompting strategy. accordingly, we compare the following approaches, 1. basic prompting (basic): in this straightforward prompting method, the user provides a clear and direct prompt asking for an appropriate response without any additional guidance or instructions about how to proceed while responding to the input query. we report our results in figure 2 . we see significant improvement in all combinations of persona agents' models and prompting strategies. in particular, the mcq prompting setup demonstrates the highest performance gain. we believe that the choices in the mcq setup help the model restrict the search space and thus make a directed answering effort. yes/no questioning also improves performance over basic questions to a significant extent. the best and worst performing model prompting strategy pair from basic questioning, namely mixtral basic and gpt4o-reflexion showed an improvement in accuracy from 39. 14 % to 52. 69 % and from 82. 02 % to 91. 96 %, respectively. ## concluding remarks llms like llama and gpt-4o are being used to generate synthetic persona agents that can mimic human behaviors, personality, and language. however, no standardized process is considered while evaluating these persona agents for correctness, diversity, and relevance in different domain use cases. contrary to existing approaches, we propose a dynamic and generic evaluation approach that is adaptable to any persona and to any domain. we dynamically generate appropriate questions considering domain constraints and requirements. we also propose an evaluation rubric useful in assessing persona agents for various higher-order thinking questions. we show that diversity is illusive while accuracy can be improved by simple and quick prompt-based mitigation strategies. we also observe a high correlation between expert and llm evaluation. in the future, we would like to explore how to improve diversity in responses. we also plan to extend the work in mitigation strategies for improving persona agents' performance in higherorder thinking skills. additionally, analyzing the model diversity, ways to improve it, and a study of diversity-performance tradeoff is a viable future direction.