Do Language Models Mirror Human Confidence? Exploring Psychological Insights to Address Overconfidence in LLM s (2025)

Abstract: Psychology research has shown that humans are poor at estimating their performance on tasks, tending towards underconfidence on easy tasks and overconfidence on difficult tasks. We examine three LLMs, Llama-3-70B-instruct, Claude-3-Sonnet, and GPT-4o, on a range of QA tasks of varying difficulty, and show that models exhibit subtle differences from human patterns of overconfidence: less sensitive to task difficulty, and when prompted to answer based on different personas—e.g., expert vs layman, or different race, gender, and ages—the models will respond with stereotypically biased confidence estimations even though their underlying answer accuracy remains the same. Based on these observations, we propose Answer-Free Confidence Estimation (AFCE) to improve confidence calibration and LLM interpretability in these settings. AFCE is a self-assessment method that employs two stages of prompting, first eliciting only confidence scores on questions, then asking separately for the answer. Experiments on the MMLU and GPQA datasets spanning subjects and difficulty show that this separation of tasks significantly reduces overconfidence and delivers more human-like sensitivity to task difficulty.

## introduction reliable confidence estimates are essential for effective human-machine collaboration @xcite . large language models (llms), however, are prone to overconfidence @xcite , which can result in inaccurate predictions when they should abstain @xcite . as these models are increasingly deployed for real-world tasks such as medical diagnosis @xcite , legal analysis @xcite , and decision support systems @xcite , their performance directly impacts outcomes that affect human this scatter plot highlights the llm's tendency to overestimate its own abilities and underestimate the performance of others (referred to as a "average person" in the experiment prompt). the llm demonstrates more accurate self-assessment compared to its evaluations of other llm role-playing roles. however, its overestimation and underplacement problems are significantly worse than that of humans. human data are from moore and healy (2008)'s paper. human overconfidence is recognized as a significant cognitive bias @xcite . @xcite reconcile experimental findings that individuals tend to (i) overestimate their own abilities on difficult tasks and underestimate them on easy tasks shown in figure 1 , and (ii) estimate that they outperform relative to others on easy tasks (overplacement) and underperform relative to others on hard tasks (underplacement). the authors @xcite explain these phenomena using an information theoretic model demonstrating individuals' regressive estimates of their performance and even more regressive estimates of others' performance: on easy tasks, they underestimate their own success and others' even more so; on hard tasks, they overestimate their own performance and others' to an even greater degree. current confidence estimation approaches such as vanilla verbalized confidence reveals persistent overconfidence and highlights a disconnect between model-reported confidence and actual task performance as shown in figure 1 . motivated by these observations, we propose answer-free confidence estimation (afce), which separates confidence estimation and answer generation into separate stages, and find that overconfidence effects as measured by expected calibration error (ece) are reduced in challenging tasks where overconfidence is pronounced. these results suggest that verbalized confidence methods should not assume human-like behavior in their design. we further use afce to explore whether confidence patterns observed in humans, as described by @xcite , are also present in llms. we uncover three distinct phenomena. (i) models' confidence scores are comparatively insensitive to task difficulty and exhibit only a weak correlation with actual accuracy, unlike human patterns reported by @xcite . (ii) when prompted with occupational personas, the model reflects stereotypical confidence levels (e.g., "layman" lower than "expert") regardless of performance. (iii) adding demographic cues (e.g., gender, race, age) further reduces expressed confidence, even when accuracy stays the same. our work investigates three core questions: (i) sensitivity to task difficulty: is a model's expressed confidence calibrated to task difficulty, and does it reflect the same over-and underestimation patterns observed in human judgment? (ii) overand underplacement across expertise: do models demonstrate placement biases when estimating the performance of others, particularly when adopting personas with varying levels of expertise? (iii) demographic bias in confidence expression: do models exhibit systematic confidence biases when conditioned on demographic attributes such as race, gender, or age? we summarize our contributions below: ## related work we review related work on human overconfidence and confidence elicitation methods for llms. human overconfidence overconfidence refers to an unjustified belief in one's knowledge and abilities @xcite , leading to undesirable outcomes in domains such as medicine @xcite , politics (van prooijen, 2021), and science @xcite . models to explain overconfidence have been broadly considered (e.g., dunning-kruger @xcite , or recent results from @xcite showing that those with intermediate knowledge may be the most overconfident). in this paper, we focus on the experiments of @xcite , whose influential unifying model explained a variety of previous findings. in this study, we adopt the overconfidence measure from @xcite including over(under)estimation and over(under)placement. confidence elicitation in language models previous methods for eliciting confidence have primarily relied on white-box approaches, which have estimated confidence using token likelihoods @xcite and internal state-based methods @xcite . while effective, these techniques require internal access to the model, making them less applicable to models served over closed apis, like gpt-4 @xcite . verbalized confidence approaches @xcite , primarily the vanilla method, appropriate to such models (i.e., prompting the model to produce confidence estimates in its output) tend to produce uniformly high estimations of model confidence, usually between 80% and 100% @xcite . to improve these estimates, some studies introduce consistency-based methods @xcite to mitigate overconfidence. other studies @xcite investigated the correlation between verbalized uncertainty and token probability and showed gpt-4o has strongest confidence-probability alignment across variety of tasks. in this study, we adopt these widely used confidence elicitation methods as baselines, but demonstrate a surprising divergence from human behavior that suggests a problematic decoupling of confidence estimation from answer generation. indeed, prompting models to estimate confidence without producing answers reduces overconfidence and outperforms baseline methods on hard tasks. roly-playing with language model llms are increasingly employed to simulate human personas. recent studies @xcite @xcite provide empirical evidence that llm-driven simulations can replicate social science experiments and online forums with consistency comparable to data ob-tained from human participants. likewise, @xcite al. (2024b); jiang et al. (2023) show that advanced role-playing agents exhibit personalities closely aligned with human perceptions, underscoring the effectiveness of role-playing approaches. such methodologies underscore the emergence of llm-based role-play as a versatile, powerful tool across multiple domains @xcite . in this paper, we consider the interaction between role-playing and confidence estimation to investigate whether llms exhibit overplacement patterns akin to those observed in humans. we also address associated limitations and ethical considerations, motivating continued research into best practices for employing llms in role-playing contexts. bias in llm-driven computational social science llms are known to poorly represent certain groups that make up a significant portion of the population (e.g., age 65+) @xcite . people perceive chatgpt as predominantly male when asked about its gender @xcite , particularly about its core capabilities such as text summarization @xcite . moreover, chatgpt has been shown to generate genderbiased responses @xcite . previous work @xcite revealed that pretrained llms do have political leanings in pretraining corpora, propagating social biases into hate speech predictions and misinformation detectors. @xcite showed recent llms contain human-like biases about right and wrong behaviors, reflecting existing ethical and moral norms of society. @xcite confirmed a substantial degree of in-group and outgroup bias of llms across languages and personas. these latent (and in some applications, explicit) demographic roles can confound confidence estimation: roles associated with over-or underperformance in the training data are associated with verbalized over-or underconfidence estimates, even when actual performance is consistent. ## data & models datasets following the experiment design of moore and healy (2008), foot_0 which examined question banks spanning various difficulty levels (high school, college, expert) and subjects (physics, chemistry, biology), we use two datasets: • mmlu @xcite , a collection of domain-specific multiple-choice questions across 57 subjects at multiple educational stages. we select mmlu questions from high school and college in the subject areas of physics, chemistry, and biology. models we examine the widely used opensource llm meta-llama-3-70b-instruct (llama-3-70b), as well as two api-based models, claude-3-sonnet-20240229 (claude-3) and gpt-4o-2024-05-13 (gpt-4o). to enhance reproducibility and limit variability in model outputs, we set temperature to 0 and top-p sampling to 1. model details are provided in appendix ## over(under) estimation across task difficulty in this section, we study the relationship between the llms' confidence estimation and task diffi-culty. we compare our answer-free confidence estimation (afce) method with other commonly used confidence estimation methods. ## over(under)placement across levels of expertise in psychology, overplacement is similar to overconfidence but refers to an inaccurate belief about one's abilities, performance, or qualities compared to others, often overestimating one's relative standing (moore and healy, 2008). ## bias in confidence estimation across demographic personas here, we combine the afce method and roleplaying to analyze variations in confidence estimation across different protected attributes (race, gender, and age). ## ablation study we investigate whether afce generalizes to openended qa and whether it remains robust to variations in question order and the number of questions within a prompt. accordingly, we conducted the following two experiments using gpt-4o. afce generalizes to open-ended generation qa. we randomly sampled 100 open-ended questions each from naturalquestions-open @xcite and simpleqa @xcite . experimental results presented in afce demonstrates robustness under variations in question order and group size. we conducted two experiments: (1) reducing the group size to five questions, and (2) randomizing question order. we observed no substantial differences in performance under either condition. as shown in ## discussion our study provides insights into llm confidence calibration informed by the psychology literature, exposing subtle differences between llm and human behavior with implications for the understanding of confidence estimation and the design of confidence calibration techniques. while our method afce effectively mitigates overconfidence in challenging tasks, it may lead to underconfidence in easier tasks. this effect could stem from the decoupling of answer generation from confidence estimation. however, the underlying mechanism by which this decoupling influences confidence estimation remains unclear. it is possible that afce adjusts confidence more aggressively for challenging questions, while its influence is attenuated for simpler ones. further investigation is needed to clarify how and why this decoupling shapes confidence across varying task difficulties. we find that llms can be easily manipulated by expert personas, leading to inflated confidence scores. this highlights key directions for future work: first, understanding how confidence signals influence human trust and decision-making in realworld settings; second, developing dynamic confidence calibration methods tailored to specific applications or user profiles to better manage risk. bias in confidence calibration across demographics, such as race, gender, and age, poses additional concerns. while recent models like gpt-4o show progress in reducing these biases, other llms still exhibit notable disparities. this warrants further investigation, as addressing confidence calibration alone may be insufficient to ensure fairness and reliability. further experiments should explore how demographic-aware verbalized confidence estimation can help mitigate bias across different groups. ## conclusion we analyze llm overconfidence from a psychological perspective, showing that llms are less sensitive to task difficulty and can drastically shift their confidence when adopting personas-despite minimal changes in actual performance. this suggests that answer generation and confidence estimation involve distinct processes differently influenced by prompts and biases. we propose afce, which significantly reduces ece for difficult reasoning questions prone to overconfidence. we also find that most llms (except gpt-4o) become underconfident when adopting asian, age 55+, or female personas, again without affecting task performance. likewise, a model's self-assessment is typically overconfident, yet it claims higher confidence as an expert and lower confidence as a novice, with no notable impact on performance.