No for Some, Yes for Others: Persona Prompts and Other Sources of False Refusal in Language Models (2025)

Abstract: Large language models (LLMs) are increasingly integrated into our daily lives and personalized. However, LLM personalization might also increase unintended side effects. Recent work suggests that persona prompting can lead models to falsely refuse user requests. However, no work has fully quantified the extent of this issue. To address this gap, we measure the impact of 15 sociodemographic personas (based on gender, race, religion, and disability) on false refusal. To control for other factors, we also test 16 different models, 3 tasks (Natural Language Inference, politeness, and offensiveness classification), and nine prompt paraphrases. We propose a Monte Carlo-based method to quantify this issue in a sample-efficient manner. Our results show that as models become more capable, personas impact the refusal rate less. However, we find that the choice of model significantly influence false refusals, especially in sensitive content tasks. The impact of certain sociodemographic personas further increases the false refusal effect in some models, which suggests that there are underlying biases in the alignment strategies or safety mechanisms.

## introduction large language models (llms) are increasingly integrated into real-world applications, allowing users to interact with them in diverse ways, from creative writing to tutoring assistants. one way to improve user experience is through personalization, so that interactions are adapted to a user's personal preferences, communication styles, and contextual needs @xcite @xcite . recent works have shown the ability of llms to embody diverse personas in their responses through prompts like "you are a very friendly and outgoing person who loves to be around others." to induce an extroverted persona @xcite . however, persona prompting can have unintended side effects on model behavior. notably, previous works have shown that persona prompting can lead models to falsely refuse user requests based on sociodemographics or cultural factors @xcite @xcite . false refusal, more generally, means models refuse safe requests, often because they superficially resemble unsafe prompts or mention sensitive topics @xcite @xcite . the disparity of false refusals across different sociodemographic personas creates unfair differences in user experiences and consequently reveals models' underlying social biases. to mitigate this problem, we first need to quantify it. this paper presents a large-scale study measuring the impact of prompting with different sociodemographic personas on false refusals. we include a total of 15 sociodemographic personas based on sociodemographic factors (gender, race, religion, and disability). to control for other contextual factors, we include a wide range of elements: three nlp tasks, 16 models, and nine prompt paraphrases. the models vary in size from small to medium and belong to different families, including meta's llama (ai@meta, 2024), google's gemma @xcite and alibaba's qwen @xcite . the three tasks are 1) natural language inference (nli), where personas should not matter (so we expect no refusal), to increasing tasks that present sensitive content and thus are likely to produce refusal, namely 2) politeness and 3) offensiveness classification. the resulting combinatorial search space is massive and cannot be exhaustively mapped. we, therefore, propose a monte carlo-based method for measuring the impact of personas across model families on false refusals in a sample-efficient manner. we find that personas and prompt variations matter more in early versions of the models. as they become more capable, these choices matter less. instead, the choice of task and model has an increasing impact on the refusal results: some tasks and some model families trigger more refusals when prompted with specific personas (like black, muslim, and transgender), indicating potential biases within the models. our findings suggest underlying biases in the alignment strategies and highlight the need for fairer alignment techniques that balance fairness and safety. however, open-ended prompts elicit more refusals across tasks. our results also show how often overlooked experimental design choices substantially influence model behavior, highlighting the need for more transparent reporting of researcher choices to improve reproducibility. otherwise, we risk incorrectly ascribing causal effects to results that were influenced by researcher choices beyond what was studied. for example, prior studies on the impact of sociodemographic personas might have produced vastly different findings had they chosen a different task or studied different models. contributions: (i) we systematically evaluate the influence of sociodemographic persona variations on model refusal rates, controlling for task choice, prompt design, and model choice; (ii) we introduce a monte carlo sampling method to quantify the impact of different sources of refusals on model false refusal behavior. this allows us to efficiently measure how different sources shape false refusals in models. (iii) we quantify the impact of the various factors on false refusals through regression and wasserstein-distance-based methods. ## sources of false refusals our central research question is "how much do persona choice and other experimental factors influence false refusal?" our starting hypothesis, based on prior work, is that personas increase false refusals at least some of the time @xcite @xcite . however, we do not expect all false refusals to be explained by personas. therefore, in addition to specific personas ( §2.1), we control for other plausible sources of false refusal -specifically task choice ( §2.2), prompt choice ( §2.3), and model choice ( §2.4). ## related work a growing body of work researches benchmarking false refusal in llms, primarily in standard open-ended chat settings. the first test suite explicitly designed for this purpose was xstest @xcite previous work on false refusal shows that safetyoptimized models often over-refuse, especially when prompted with personas. @xcite evaluate llama2 safety measures using nontoxic prompts and show response disparities across sociodemographic groups. @xcite show that gpt3 and llama2 models sometimes refuse to answer when prompted with personas, pointing out encoded biases in models. plaza-del-arco et al. (2024) find significant false refusal disparities in llms while prompting with religious personas for emotion attribution, with llama2 models showing higher refusal rates for some groups. de @xcite show that false refusals are arbitrary and disparate, varying across similar personas and sociodemographics, though their main focus was on llms' task performance, biases, and attitudes. unlike previous work, our paper investigates false refusals across sociodemographics, while also considering task, prompt, and model choices. we analyze 16 models from nine families, allowing us to test how false refusals have evolved over time and vary across model families and scales. ## conclusion in this paper, we measure how prompting with different sociodemographic personas impacts false refusals, controlling for other contextual factors like model, task, and prompt choices. we find that false refusals vary widely across these factors, with model choice being the most influential, followed by task, persona and prompts. we find that newer model families have fewer false refusals than earlier iterations. however, this trend is not consistent across models; newer gemma versions show a concerning increase compared to older models. our results show that tasks with sensitive content trigger more false refusals than objective tasks like nli. furthermore, we find that persona-based prompting affects false refusals, especially among particular groups related to race, gender, and religion. our findings contribute to the broader effort of measuring these issues and identifying ongoing challenges to improve safety and fairness in llms. they also serve as a reminder that unaccounted factors can substantially influence model behavior. the risk is that unreported factors distort reported results. our findings strongly suggest that llm results need to be more fully documented to avoid replication issues.