Measuring Stereotype and Deviation Biases in Large Language Models

• URL: http://arxiv.org/abs/2508.06649v2
• Date: Mon, 18 Aug 2025 20:38:45 GMT
• Title: Measuring Stereotype and Deviation Biases in Large Language Models
• Authors: Daniel Wang, Eli Brignac, Minjia Mao, Xiao Fang,
• Abstract summary: Large language models (LLMs) are widely applied across diverse domains, raising concerns about their limitations and potential risks.<n>This study investigates two types of bias that LLMs may display: stereotype bias and deviation bias.
• Score: 1.8311821879979955
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) are widely applied across diverse domains, raising concerns about their limitations and potential risks. In this study, we investigate two types of bias that LLMs may display: stereotype bias and deviation bias. Stereotype bias refers to when LLMs consistently associate specific traits with a particular demographic group. Deviation bias reflects the disparity between the demographic distributions extracted from LLM-generated content and real-world demographic distributions. By asking four advanced LLMs to generate profiles of individuals, we examine the associations between each demographic group and attributes such as political affiliation, religion, and sexual orientation. Our experimental results show that all examined LLMs exhibit both significant stereotype bias and deviation bias towards multiple groups. Our findings uncover the biases that occur when LLMs infer user attributes and shed light on the potential harms of LLM-generated outputs.

Related papers

• The Biased Samaritan: LLM biases in Perceived Kindness [0.0]
  Large Language Models (LLMs) have become ubiquitous in many fields.<n>This paper provides a novel method for evaluating the demographic biases of various generative AI models.
  arXiv  Detail & Related papers  (2025-06-12T23:33:42Z)
• Veracity Bias and Beyond: Uncovering LLMs' Hidden Beliefs in Problem-Solving Reasoning [4.452208564152158]
  Despite human value-aligned models' alignment against demographic stereotypes, they have been shown to exhibit biases under various social contexts.<n>We show two forms of such veracity biases: Attribution Bias, where models disproportionately attribute correct solutions to certain demographic groups, and Evaluation Bias, where models' assessment of identical solutions varies based on perceived demographic authorship.<n>Our findings indicate that demographic bias extends beyond surface-level stereotypes and social context provocations, raising concerns about LLMs' deployment in educational and evaluation settings.
  arXiv  Detail & Related papers  (2025-05-22T02:13:48Z)
• Beyond Partisan Leaning: A Comparative Analysis of Political Bias in Large Language Models [6.549047699071195]
  This study adopts a persona-free, topic-specific approach to evaluate political behavior in large language models.<n>We analyze responses from 43 large language models developed in the U.S., Europe, China, and the Middle East.<n>Findings show most models lean center-left or left ideologically and vary in their nonpartisan engagement patterns.
  arXiv  Detail & Related papers  (2024-12-21T19:42:40Z)
• How far can bias go? -- Tracing bias from pretraining data to alignment [54.51310112013655]
  This study examines the correlation between gender-occupation bias in pre-training data and their manifestation in LLMs.<n>Our findings reveal that biases present in pre-training data are amplified in model outputs.
  arXiv  Detail & Related papers  (2024-11-28T16:20:25Z)
• Large Language Models Reflect the Ideology of their Creators [71.65505524599888]
  Large language models (LLMs) are trained on vast amounts of data to generate natural language.<n>This paper shows that the ideological stance of an LLM appears to reflect the worldview of its creators.
  arXiv  Detail & Related papers  (2024-10-24T04:02:30Z)
• LLMs are Biased Teachers: Evaluating LLM Bias in Personalized Education [6.354025374447606]
  We evaluate large language models (LLMs) for bias in the personalized educational setting.<n>We reveal significant biases in how models generate and select educational content tailored to different demographic groups.
  arXiv  Detail & Related papers  (2024-10-17T20:27:44Z)
• Towards Implicit Bias Detection and Mitigation in Multi-Agent LLM Interactions [25.809599403713506]
  Large Language Models (LLMs) are increasingly being employed in numerous studies to simulate societies and execute diverse social tasks. LLMs are susceptible to societal biases due to their exposure to human-generated data. This study investigates the presence of implicit gender biases in multi-agent LLM interactions and proposes two strategies to mitigate these biases.
  arXiv  Detail & Related papers  (2024-10-03T15:28:05Z)
• A Multi-LLM Debiasing Framework [85.17156744155915]
  Large Language Models (LLMs) are powerful tools with the potential to benefit society immensely, yet, they have demonstrated biases that perpetuate societal inequalities. Recent research has shown a growing interest in multi-LLM approaches, which have been demonstrated to be effective in improving the quality of reasoning. We propose a novel multi-LLM debiasing framework aimed at reducing bias in LLMs.
  arXiv  Detail & Related papers  (2024-09-20T20:24:50Z)
• Investigating Bias in LLM-Based Bias Detection: Disparities between LLMs and Human Perception [13.592532358127293]
  We investigate the presence and nature of bias within Large Language Models (LLMs)<n>We probe whether LLMs exhibit biases, particularly in political bias prediction and text continuation tasks.<n>We propose debiasing strategies, including prompt engineering and model fine-tuning.
  arXiv  Detail & Related papers  (2024-03-22T00:59:48Z)
• Bias and Volatility: A Statistical Framework for Evaluating Large Language Model's Stereotypes and the Associated Generation Inconsistency [33.17945055081054]
  Current alignment evaluation metrics often overlook stereotypes' randomness caused by large language models' inconsistent generative behavior.<n>We propose the Bias-Volatility Framework (BVF), which estimates the probability distribution of stereotypes in LLM outputs.
  arXiv  Detail & Related papers  (2024-02-23T18:15:56Z)
• A Theory of Response Sampling in LLMs: Part Descriptive and Part Prescriptive [53.08398658452411]
  Large Language Models (LLMs) are increasingly utilized in autonomous decision-making.<n>We show that this sampling behavior resembles that of human decision-making.<n>We show that this deviation of a sample from the statistical norm towards a prescriptive component consistently appears in concepts across diverse real-world domains.
  arXiv  Detail & Related papers  (2024-02-16T18:28:43Z)
• Large Language Models are Geographically Biased [47.88767211956144]
  We study what Large Language Models (LLMs) know about the world we live in through the lens of geography. We show various problematic geographic biases, which we define as systemic errors in geospatial predictions.
  arXiv  Detail & Related papers  (2024-02-05T02:32:09Z)
• Exploring the Jungle of Bias: Political Bias Attribution in Language Models via Dependency Analysis [86.49858739347412]
  Large Language Models (LLMs) have sparked intense debate regarding the prevalence of bias in these models and its mitigation. We propose a prompt-based method for the extraction of confounding and mediating attributes which contribute to the decision process. We find that the observed disparate treatment can at least in part be attributed to confounding and mitigating attributes and model misalignment.
  arXiv  Detail & Related papers  (2023-11-15T00:02:25Z)
• Bias Runs Deep: Implicit Reasoning Biases in Persona-Assigned LLMs [67.51906565969227]
  We study the unintended side-effects of persona assignment on the ability of LLMs to perform basic reasoning tasks. Our study covers 24 reasoning datasets, 4 LLMs, and 19 diverse personas (e.g. an Asian person) spanning 5 socio-demographic groups.
  arXiv  Detail & Related papers  (2023-11-08T18:52:17Z)
• Do LLMs exhibit human-like response biases? A case study in survey design [66.1850490474361]
  We investigate the extent to which large language models (LLMs) reflect human response biases, if at all. 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.
  arXiv  Detail & Related papers  (2023-11-07T15:40:43Z)
• Probing Explicit and Implicit Gender Bias through LLM Conditional Text Generation [64.79319733514266]
  Large Language Models (LLMs) can generate biased and toxic responses. We propose a conditional text generation mechanism without the need for predefined gender phrases and stereotypes.
  arXiv  Detail & Related papers  (2023-11-01T05:31:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.