Mixture-of-Personas Language Models for Population Simulation (2025)

Abstract: Advances in Large Language Models (LLMs) paved the way for their emerging applications in various domains, such as human behavior simulations, where LLMs could augment human-generated data in social science research and machine learning model training. However, pretrained LLMs often fail to capture the behavioral diversity of target populations due to the inherent variability across individuals and groups. To address this, we propose Mixture of Personas (MoP), a probabilistic prompting method that aligns LLM responses with the target population. MoP is a contextual mixture model, where each component is an LM agent characterized by a persona and an exemplar that represents the behaviors of subpopulation. The persona and the exemplar are randomly chosen according to the learned mixing weights to elicit diverse LLM responses during simulation. MoP is flexible, does not require model fine-tuning, and is transferable between base models. Experiments for synthetic data generation show that MoP outperforms competing methods in alignment and diversity metrics.

## introduction the impressive capability of large language models (llms) to generate human-like output has enabled their application across various domains, where their responses can complement or substitute human-generated data, providing a scalable approach to address data limitations @xcite . prominent examples include simulating human behaviors in social science @xcite , modeling economic agents and decision-making in economics @xcite , analyzing political trends and electoral dynamics in political science @xcite , or generating synthetic training data @xcite . natural human responses in those applications often reflect diverse behaviors or preferences of different personas shaped by demographic, cultural, and societal variations of the target population @xcite . modeling the distribution of these behaviors is crucial for generating realistic and contextually relevant output @xcite . a common approach to achieving tailored responses is to prompt llms with a persona that simulates a specific group's behavior, language, and preferences; see figure 1 . however, recent studies show that llm's responses often lack diversity and exhibit significant biases @xcite , and this downside persists even when llms are prompted with a persona @xcite . some efforts have been made toward improving the steerability llms, enabling them to produce outputs aligned with specific user intentions or personas. approaches to steerability generally fall into two main categories: prompt engineering @xcite and fine-tuning using personalized datasets @xcite @xcite . although these methods aim to capture the behaviors, preferences, and communication styles of particular user groups, both techniques face distinct challenges. personalized prompt engineering for each user is difficult to tune and resource-intensive. meanwhile, learning-based methods require access to personal data, which is often scarce or expensive. the reliance on personal data also raises privacy concerns, restricting the practical applicability of these approaches. furthermore, diversity sampling remains a notable challenge in llms, especially when simulating responses representative of diverse populations. current methods @xcite typically rely on a fixed, optimal selection of few-shot examples reused across multiple downstream tasks, complemented primarily by temperature scaling to enhance response diversity. however, relying solely on temperature scaling can be inadequate for generating semantically diverse outputs, frequently resulting in a trade-off between quality and diversity or causing outputs to collapse into semantically similar responses @xcite . proposed work. we address the steerability problem in llms, focusing on aligning model responses with the characteristics of a target population. to achieve this, we propose the mixture of persona (mop), a probabilistic prompting framework that leverages persona descriptions and incontext exemplars to steer responses. mop functions as a contextual mixture model comprising multiple llm agents, each characterized by a persona prompt that is either user-defined or synthesized from observed target population responses. during response generation, personas are probabilistically selected on the basis of mixing weights, enabling the model to produce customized and contextually relevant outputs. since the naive mop approach described above may still be susceptible to biases and limited response diversity, we improve it by incorporating in-context examples to better align llm responses with population characteristics. these in-context exemplars are drawn anonymously from a representative pool of the target population, guided by learnable weights. importantly, our method does not require direct associations between individual personas and specific examples, thus minimizing reliance on personal data. instead, each exemplar's influence on a persona is controlled through a secondary set of mixing weights, forming a two-level hierarchical mixture model. the first level manages persona selection, while the second level determines exemplar weighting. this hierarchical structure enables mop to effectively represent the diversity and complexity inherent in populationlevel behaviors, simultaneously mitigating biases in llm-generated responses. we conduct extensive experiments to evaluate the effectiveness of mop against existing prompting methods. specifically, our experiments span two main scenarios: (1) simulating human-like opinions in tasks such as generating movie and restaurant reviews as well as news articles, and (2) creating synthetic data for downstream classification tasks. the results demonstrate that mop significantly enhances alignment with target population responses, achieving a 58% improvement in fid scores and a 28% increase in mauve scores compared to the strongest baseline. additionally, mop generates more diverse responses, effectively capturing nuanced variations in population-level behavior without compromising response quality. we further demonstrate the transferability of mop: a model trained on llama3-8b-instruct can directly generalize to other base models such as gemma-9b-instruct and mistral-7b-instruct in a plug-andplay fashion, eliminating the need for retraining. ## problem setting we consider the problem of simulating human behaviors or preferences at the population level using prior knowledge from pre-trained llms @xcite . let p be a population composed of k groups of interest. each group @xmath0, . . . , k} can be characterized by a persona g k that reflects the traits and motivations driving the behaviors of individuals within that group. let @xmath1, y i )} n be a recorded data set from the population of interest p. for synthetic data generation tasks such as movie review generation, x i could be an input context (e.g., movie title), and y i could be a human response (e.g., a movie review). note that a given input x could be associated with multiple and diverse responses y produced by different individuals in the population p with different preferences. in real-world applications, the persona description {g k } k can be pre-defined by the users according to the task at hand or automatically synthesized from the record set d. we will consider both variants in the experiments. it is worth noting further that, different from @xcite and choi and li (2024), we do not require access to preference or personal data generated by persona g k (e.g., g k -(x i , y i ) pairs); therefore, our setting could be considered as an 'unsupervised' setting of the steerability problem of llms. ## methodology in this section, we propose the mixture of personas (mop), a contextual mixture model of lm agents designed to simulate the diverse preferences of individuals within a target population p. in mop, each agent is characterized by a persona prompt that encapsulates a specific subgroup's aggregated behaviors or preferences. furthermore, the agent's responses are guided by an exemplar, randomly selected from the anonymous data set d based on a learnable mixing weight, making mop a two-level hierarchical mixture of llm agents. the overall pipeline is depicted in figure 2 . for clarity, we assume that the persona descriptions {g k } k are pre-defined, postponing the discussion of persona synthesis to the end of this section. ## experiments in this section, we conduct extensive experiments to investigate the effectiveness of the proposed methods, with the aim of answering the following three research questions: rq1: can mop steer the output of llms towards the population of interest? rq2: can we utilize the mop prompting technique to generate high-quality data for training ml models on task-specific applications? rq3: can mop be transferable to different pretrained llms in a plug-and-play manner? to answer the above research questions, we conduct two main experiments: steerability: we generate news articles and movie/restaurant reviews to demonstrate that a pre-trained llm with mop prompting can simulate the distribution of humangenerated content while maintaining its diversity. synthetic data for training ml models: we show that adapting mop prompting can create highquality training samples for classification tasks, such as topic or sentiment classification. ## conclusion we introduced mixture of personas (mop), a probabilistic prompting method designed to align llmgenerated responses with a target population. our experiments showed that exemplar-based variants of mop significantly improve the alignment and diversity of synthetic data, leading to enhanced performance in downstream tasks. furthermore, mop is flexible and does not require fine-tuning of the base model, enabling seamless transferability across different models without retraining.