Quantifying the Persona Effect in LLM Simulations

Position: Synthetic Persona Needs Explicit Grounding and Standardized Reporting

This position paper argues that synthetic personas used in Large Language Models (LLM) social simulations need explicit grounding. By grounding, we mean that researchers should state where persona information comes from, what social actor or population it is meant to represent, what evidence supports it, and what kinds of simulation claims it can justify. Without such grounding, persona-based simulations risk treating appearance of plausible descriptions or vivid narratives as evidence of realism. We identify several recurring failure modes that explicit grounding can help expose, and argue that these failures should not be diagnosed only after simulation results are produced; they should be addressed at the level of persona construction and reporting. We conclude by proposing lightweight reporting standards for persona provenance, construction process, selection or sampling logic, internal consistency checks, model enactment checks, and intended inference.

Activation-Steered Personas in Task-Oriented LLM Agent Simulations

Large language models are increasingly used to simulate both user and system behavior in task-oriented dialogue (TOD). Yet personality control in these simulations remains largely prompt-based, making trait expression difficult to regulate and verify across dialogue turns. We investigate activation steering as an inference-time mechanism for inducing and adapting personality in TOD simulation. In our framework, user-side steering (US) applies fixed Big Five trait-pole activation vectors with scalar coefficients to generate users with different trait intensities. System-side adaptive steering (SA) dynamically adapts the system agent at each turn by predicting system-side steering coefficients from the dialogue context. We evaluate four steering conditions: an unsteered Baseline, SA, US, and US+SA, using the Schema-Guided Dialogue benchmark across the Restaurants and Movies domains. Qwen2.5-7B-Instruct is used as the primary model, and Llama-3.1-8B-Instruct is used as a robustness setting. Our results show that activation-level personality control affects TOD behavior, but its benefits depend on domain, trait observability, and steering strength. Compared with the unsteered Baseline, SA improves standard task metrics slightly, with larger gains in pairwise-judged user satisfaction and truthfulness, mainly in Restaurants, where dialogues contain richer personality-relevant slots and preferences. User-side steering produces observable personality expression only for some traits: socially or affectively expressive traits such as careless, outgoing, compassionate, and nervous become more detectable at stronger steering, while consistent and solitary remain difficult to observe in short TOD interactions. Adding SA to steered users yields modest task-level changes but a statistically significant improvement in pairwise-judged truthfulness (+8.3 pp) and user satisfaction (+2.7 pp). System-side composition analysis further shows that SA does not directly mirror the user's trait; it usually defaults to Conscientiousness-centered system behavior and becomes more socially adaptive when user trait expression is observable especially at higher steering scalar. Overall, activation steering is a viable alternative to prompt-based personality conditioning for controlled TOD simulation, but its effects must be evaluated with respect to trait observability, steering strength, and the personality relevance of the task domain.

Can LLMs Faithfully Enact Superforecaster Personas?

Superforecasters are people that make forecasts that are statistically better than the average person; they offer an interesting testbed for persona modelling. In this preliminary work, we investigate whether large language models (LLMs) can faithfully model superforecaster-like personas making similar forecasts and generating similar rationales behind the forecasts. When we compare LLM-generated responses to the ground truth responses of superforecasters, we find that few-shot prompting generates numerically closer forecasts than instruction-prompting baselines. We also measure the similarity of generated and real rationales, finding that the rationales generated based on examples are judged more similar to those of superforecasters than instruction-prompting baselines. However, an analysis of the reasoning patterns in the rationales shows significant differences between human and LLM forecasters, pointing to a gap between stylistic imitation and deep reasoning similarity. These preliminary results raise several interesting questions about how getting superforecaster-like behaviour from LLMs actually works, and open new avenues to explore for improving the forecasting behaviour of LLMs.

Large language models outperform humans at estimating society's everyday norms—but hybrids are even better

As AI assistants and social robots enter human environments, their ability to navigate context-dependent social norms is essential to avoid harm and ensure successful collaboration. We evaluate six large language models (LLMs) on their ability to estimate American social norms across 555 everyday scenarios (measured in prior work) and compare these to estimates from 320 humans. LLMs achieve remarkably high accuracy, clearly outperforming the average human. However, the errors LLMs make are systematic; they are similar across runs of the same LLM and even across different LLMs. As a consequence of this homogeneity, aggregating estimates of LLMs produces little improvement. Individual humans make much worse estimates, often defaulting to extreme right-or-wrong judgments even when asked to estimate population averages, but their errors are idiosyncratic and, consequently, aggregating their estimates yields dramatic improvement through wisdom-of-crowds effects. As humans make different errors than LLMs, hybrid ensembles combining both substantially outperform either alone.

Do language models accommodate their users? A study of linguistic convergence

While large language models (LLMs) are generally considered proficient in generating language, how similar their language usage is to that of humans remains understudied. In this paper, we test whether models exhibit linguistic convergence, a core pragmatic element of human language communication: do models adapt, or converge, to the linguistic patterns of their user? To answer this, we systematically compare model completions of existing dialogues to original human responses across sixteen language models, three dialogue corpora, and various stylometric features. We find that models strongly converge to the conversation's style, often significantly overfitting relative to the human baseline. While convergence patterns are often feature-specific, we observe consistent shifts in convergence across modeling settings, with instruction-tuned and larger models converging less than their pretrained and smaller counterparts. Given the differences in human and model convergence patterns, we hypothesize that the underlying mechanisms driving these behaviors are very different.

Do language models accommodate their users? A study of linguistic convergence

While large language models (LLMs) are generally considered proficient in generating language, how similar their language usage is to that of humans remains understudied. In this paper, we test whether models exhibit linguistic convergence, a core pragmatic element of human language communication: do models adapt, or converge, to the linguistic patterns of their user? To answer this, we systematically compare model completions of existing dialogues to original human responses across sixteen language models, three dialogue corpora, and various stylometric features. We find that models strongly converge to the conversation's style, often significantly overfitting relative to the human baseline. While convergence patterns are often feature-specific, we observe consistent shifts in convergence across modeling settings, with instruction-tuned and larger models converging less than their pretrained and smaller counterparts. Given the differences in human and model convergence patterns, we hypothesize that the underlying mechanisms driving these behaviors are very different.

Do LLMs Exhibit Human-like Response Biases? A Case Study in Survey Design

Abstract One widely cited barrier to the adoption of LLMs as proxies for humans in subjective tasks is their sensitivity to prompt wording—but interestingly, humans also display sensitivities to instruction changes in the form of response biases. We investigate the extent to which LLMs reflect human response biases, if at all. We look to survey design, where human response biases caused by changes in the wordings of “prompts” have been extensively explored in social psychology literature. Drawing from these works, we design a dataset and framework to evaluate whether LLMs exhibit human-like response biases in survey questionnaires. Our comprehensive evaluation of nine models shows that popular open and commercial LLMs generally fail to reflect human-like behavior, particularly in models that have undergone RLHF. Furthermore, even if a model shows a significant change in the same direction as humans, we find that they are sensitive to perturbations that do not elicit significant changes in humans. These results highlight the pitfalls of using LLMs as human proxies, and underscore the need for finer-grained characterizations of model behavior.1

Beyond Cooperative Simulators: Generating Realistic User Personas for Robust Evaluation of LLM Agents

Large Language Model (LLM) agents are increasingly deployed in settings where they interact with a wide variety of people, including users who are unclear, impatient, or reluctant to share information. However, collecting real interaction data at scale remains expensive. The field has turned to LLM-based user simulators as stand-ins, but these simulators inherit the behavior of their underlying models: cooperative and homogeneous. Agents that appear strong in simulation often fail under the unseen, diverse communication patterns of real users. To narrow this gap, we introduce Persona Policies (PPol), a plug-and-play layer that induces realistic behavioral variation in user simulators while preserving original task goals. We cast persona generation as an LLM-driven evolutionary program search that optimizes a Python generator to discover behaviors and translate them into task-preserving roleplay policies. Candidate generators are guided by a multi-objective fitness score combining human-likeness with broad coverage of human behavioral patterns. Once optimized, the generator produces a diverse population of human-like personas for any task in the domain. Across τ²-bench retail and airline domains, evolved PPol programs yield 33–62% absolute gains in fitness score over the baseline simulator. In a blinded evaluation, annotators rated PPol-conditioned users as human 80.4% of the time, close to real human traces and nearly twice as frequently as baseline simulators. Agents trained with PPol are more robust to challenging, out-of-distribution behaviors, improving task success by +17% relative to agents trained only on existing simulated interactions. This offers a novel approach to strengthen simulator-based evaluation and training without changing tasks or rewards.

PB-GRPO: Learning Socially Adaptive LLM Agents from Persona-Driven Simulation with Preference-Batched GRPO

Building LLMs that behave well socially, not merely correctly, requires more than producing locally helpful responses. A socially competent agent must infer users’ unstated goals, respect their preferences, adapt as the conversation unfolds, and know when to ask rather than act. These behaviors are inherently multi-turn and social, making them hard to optimize: real interaction data is scarce, and user preferences are typically latent rather than directly observable. To address these challenges, we build on a persona-driven social simulation environment (consisting of a persona library, LLM-based user simulators, and a user-satisfaction scoring system ranging from [0, 1]), to introduce preference-batched GRPO (PB-GRPO), a post-training algorithm that learns socially adaptive policies from conversation-level feedback. Compared to vanilla GRPO, PB-GRPO computes advantages using a normalization estimated across a bucket of users with similar preferences, stabilizing training across a diverse social population. Empirical evidence shows that PB-GRPO improves models’ social behavior over strong reinforcement-learning baselines in our simulated environment.

How LLMs Distort Our Written Language

Large language models (LLMs) are used by over a billion people globally, most often to assist with writing. In this work, we demonstrate that LLMs not only alter the voice and tone of human writing but also consistently alter the intended meaning. First, we conduct a human user study to understand how people actually interact with LLMs when using them for writing. Our findings reveal that extensive LLM use led to a nearly 70% increase in essays that remained neutral in answering the topic question. Significantly more heavy LLM users reported that the writing was less creative and not in their voice. Next, using a dataset of human-written essays that was collected in 2021 before the widespread release of LLMs, we study how asking an LLM to revise the essay based on the human-written feedback in the dataset induces large changes in the resulting content and meaning. We find that even when LLMs are prompted with expert feedback and asked to only make grammar edits, they still change the text in a way that significantly alters its semantic meaning. We then examine LLM-generated text in the wild, specifically focusing on the 21% of AI-generated scientific peer reviews at a recent top AI conference. We find that LLM-generated reviews place significantly less weight on clarity and significance of the research, and assign scores that, on average, are a full point higher. These findings highlight a misalignment between the perceived benefit of AI use and an implicit, consistent effect on the semantics of human writing, motivating future work on how widespread AI writing will affect our cultural and scientific institutions.

How LLMs Distort Our Written Language

Large language models (LLMs) are used by over a billion people globally, most often to assist with writing. In this work, we demonstrate that LLMs not only alter the voice and tone of human writing but also consistently alter the intended meaning. First, we conduct a human user study to understand how people actually interact with LLMs when using them for writing. Our findings reveal that extensive LLM use led to a nearly 70% increase in essays that remained neutral in answering the topic question. Significantly more heavy LLM users reported that the writing was less creative and not in their voice. Next, using a dataset of human-written essays that was collected in 2021 before the widespread release of LLMs, we study how asking an LLM to revise the essay based on the human-written feedback in the dataset induces large changes in the resulting content and meaning. We find that even when LLMs are prompted with expert feedback and asked to only make grammar edits, they still change the text in a way that significantly alters its semantic meaning. We then examine LLM-generated text in the wild, specifically focusing on the 21% of AI-generated scientific peer reviews at a recent top AI conference. We find that LLM-generated reviews place significantly less weight on clarity and significance of the research, and assign scores that, on average, are a full point higher. These findings highlight a misalignment between the perceived benefit of AI use and an implicit, consistent effect on the semantics of human writing, motivating future work on how widespread AI writing will affect our cultural and scientific institutions.

Do Large Language Models Perform the Way People Expect? Measuring the Human Generalization Function

What makes large language models (LLMs) impressive is also what makes them hard to evaluate: their diversity of uses. To evaluate these models, we must understand the purposes they will be used for. We consider a setting where these deployment decisions are made by people, and in particular, people's beliefs about where an LLM will perform well. We model such beliefs as the consequence of a human generalization function: having seen what an LLM gets right or wrong, people generalize to where else it might succeed. We collect a dataset of 19K examples of how humans make generalizations across 79 tasks from the MMLU and BIG-Bench benchmarks. We show that the human generalization function can be predicted using NLP methods: people have consistent structured ways to generalize. We then evaluate LLM alignment with the human generalization function. Our results show that -- especially for cases where the cost of mistakes is high -- more capable models (e.g. GPT-4) can do worse on the instances people choose to use them for, exactly because they are not aligned with the human generalization function.

Artificial Artificial Artificial Intelligence: Crowd Workers Widely Use Large Language Models for Text Production Tasks

Large language models (LLMs) are remarkable data annotators. They can be used to generate high-fidelity supervised training data, as well as survey and experimental data. With the widespread adoption of LLMs, human gold--standard annotations are key to understanding the capabilities of LLMs and the validity of their results. However, crowdsourcing, an important, inexpensive way to obtain human annotations, may itself be impacted by LLMs, as crowd workers have financial incentives to use LLMs to increase their productivity and income. To investigate this concern, we conducted a case study on the prevalence of LLM usage by crowd workers. We reran an abstract summarization task from the literature on Amazon Mechanical Turk and, through a combination of keystroke detection and synthetic text classification, estimate that 33-46% of crowd workers used LLMs when completing the task. Although generalization to other, less LLM-friendly tasks is unclear, our results call for platforms, researchers, and crowd workers to find new ways to ensure that human data remain human, perhaps using the methodology proposed here as a stepping stone. Code/data: https://github.com/epfl-dlab/GPTurk