MIST: Towards Multi-dimensional Implicit BiaS Evaluation of LLMs via Theory of Mind

• URL: http://arxiv.org/abs/2506.14161v2
• Date: Wed, 08 Oct 2025 13:24:05 GMT
• Title: MIST: Towards Multi-dimensional Implicit BiaS Evaluation of LLMs via Theory of Mind
• Authors: Yanlin Li, Hao Liu, Huimin Liu, Kun Wang, Yinwei Wei, Yupeng Hu,
• Abstract summary: Theory of Mind (ToM) in Large Language Models (LLMs) refers to their capacity for reasoning about mental states.<n>We propose an evaluation framework that leverages the Stereotype Content Model (SCM) to reconceptualize bias as a multi-dimensional failure in ToM across Competence, Sociability, and Morality.
• Score: 27.209638457499427
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Theory of Mind (ToM) in Large Language Models (LLMs) refers to their capacity for reasoning about mental states, yet failures in this capacity often manifest as systematic implicit bias. Evaluating this bias is challenging, as conventional direct-query methods are susceptible to social desirability effects and fail to capture its subtle, multi-dimensional nature. To this end, we propose an evaluation framework that leverages the Stereotype Content Model (SCM) to reconceptualize bias as a multi-dimensional failure in ToM across Competence, Sociability, and Morality. The framework introduces two indirect tasks: the Word Association Bias Test (WABT) to assess implicit lexical associations and the Affective Attribution Test (AAT) to measure covert affective leanings, both designed to probe latent stereotypes without triggering model avoidance. Extensive experiments on 8 State-of-the-Art LLMs demonstrate our framework's capacity to reveal complex bias structures, including pervasive sociability bias, multi-dimensional divergence, and asymmetric stereotype amplification, thereby providing a more robust methodology for identifying the structural nature of implicit bias.

Related papers

• Inference-Time Reasoning Selectively Reduces Implicit Social Bias in Large Language Models [0.0]
  We study how reasoning-enabled inference affects implicit bias in large language models (LLMs)<n>We find that enabling reasoning significantly reduces measured implicit bias on an IAT-style evaluation for some model classes across fifteen stereotype topics.<n>This work highlights how theory from cognitive science and psychology can complement AI evaluation research by providing methodological and interpretive frameworks.
  arXiv  Detail & Related papers  (2026-02-04T16:44:23Z)
• Beacon: Single-Turn Diagnosis and Mitigation of Latent Sycophancy in Large Language Models [0.0]
  Large language models internalize a structural trade-off between truthfulness and obsequious flattery.<n>This latent bias, known as sycophancy, manifests as a preference for user agreement over principled reasoning.<n>We introduce Beacon, a single-turn forced-choice benchmark that isolates this bias independent of conversational context.
  arXiv  Detail & Related papers  (2025-10-19T06:36:57Z)
• Relative Bias: A Comparative Framework for Quantifying Bias in LLMs [29.112649816695203]
  Relative Bias is a method designed to assess how an LLM's behavior deviates from other LLMs within a specified target domain.<n>We introduce two complementary methodologies: (1) Embedding Transformation analysis, which captures relative bias patterns through sentence representations over the embedding space, and (2) LLM-as-a-Judge, which employs a language model to evaluate outputs comparatively.<n>Applying our framework to several case studies on bias and alignment scenarios following by statistical tests for validation, we find strong alignment between the two scoring methods.
  arXiv  Detail & Related papers  (2025-05-22T01:59:54Z)
• Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMs [25.62533031580287]
  Bias in Large Language Models (LLMs) significantly undermines their reliability and fairness.<n>We propose BiasLens, a test-set-free bias analysis framework based on the structure of the model's vector space.
  arXiv  Detail & Related papers  (2025-05-21T13:50:23Z)
• Fairness Mediator: Neutralize Stereotype Associations to Mitigate Bias in Large Language Models [66.5536396328527]
  LLMs inadvertently absorb spurious correlations from training data, leading to stereotype associations between biased concepts and specific social groups.<n>We propose Fairness Mediator (FairMed), a bias mitigation framework that neutralizes stereotype associations.<n>Our framework comprises two main components: a stereotype association prober and an adversarial debiasing neutralizer.
  arXiv  Detail & Related papers  (2025-04-10T14:23:06Z)
• Cognitive Debiasing Large Language Models for Decision-Making [71.2409973056137]
  Large language models (LLMs) have shown potential in supporting decision-making applications.<n>We propose a cognitive debiasing approach, self-adaptive cognitive debiasing (SACD)<n>Our method follows three sequential steps -- bias determination, bias analysis, and cognitive debiasing -- to iteratively mitigate potential cognitive biases in prompts.
  arXiv  Detail & Related papers  (2025-04-05T11:23:05Z)
• BiasConnect: Investigating Bias Interactions in Text-to-Image Models [73.76853483463836]
  We introduce BiasConnect, a novel tool designed to analyze and quantify bias interactions in Text-to-Image models.<n>Our method provides empirical estimates that indicate how other bias dimensions shift toward or away from an ideal distribution when a given bias is modified.<n>We demonstrate the utility of BiasConnect for selecting optimal bias mitigation axes, comparing different TTI models on the dependencies they learn, and understanding the amplification of intersectional societal biases in TTI models.
  arXiv  Detail & Related papers  (2025-03-12T19:01:41Z)
• Estimating Commonsense Plausibility through Semantic Shifts [66.06254418551737]
  We propose ComPaSS, a novel discriminative framework that quantifies commonsense plausibility by measuring semantic shifts.<n> Evaluations on two types of fine-grained commonsense plausibility estimation tasks show that ComPaSS consistently outperforms baselines.
  arXiv  Detail & Related papers  (2025-02-19T06:31:06Z)
• SB-Bench: Stereotype Bias Benchmark for Large Multimodal Models [42.54907891780377]
  Stereotype biases in Large Multimodal Models (LMMs) perpetuate harmful societal prejudices.<n>Existing datasets evaluating stereotype biases in LMMs often lack diversity and rely on synthetic images.<n>We introduce the Stereotype Bias Benchmark (SB-bench), the most comprehensive framework to date for assessing stereotype biases.
  arXiv  Detail & Related papers  (2025-02-12T20:41:53Z)
• Causality can systematically address the monsters under the bench(marks) [64.36592889550431]
  Benchmarks are plagued by various biases, artifacts, or leakage.<n>Models may behave unreliably due to poorly explored failure modes.<n> causality offers an ideal framework to systematically address these challenges.
  arXiv  Detail & Related papers  (2025-02-07T17:01:37Z)
• Explicit vs. Implicit: Investigating Social Bias in Large Language Models through Self-Reflection [18.625071242029936]
  Large Language Models (LLMs) have been shown to exhibit various biases and stereotypes in their generated content.<n>This paper presents a systematic framework to investigate and compare explicit and implicit biases in LLMs.
  arXiv  Detail & Related papers  (2025-01-04T14:08:52Z)
• On the Fairness, Diversity and Reliability of Text-to-Image Generative Models [68.62012304574012]
  multimodal generative models have sparked critical discussions on their reliability, fairness and potential for misuse.<n>We propose an evaluation framework to assess model reliability by analyzing responses to global and local perturbations in the embedding space.<n>Our method lays the groundwork for detecting unreliable, bias-injected models and tracing the provenance of embedded biases.
  arXiv  Detail & Related papers  (2024-11-21T09:46:55Z)
• The Root Shapes the Fruit: On the Persistence of Gender-Exclusive Harms in Aligned Language Models [91.86718720024825]
  We center transgender, nonbinary, and other gender-diverse identities to investigate how alignment procedures interact with pre-existing gender-diverse bias.<n>Our findings reveal that DPO-aligned models are particularly sensitive to supervised finetuning.<n>We conclude with recommendations tailored to DPO and broader alignment practices.
  arXiv  Detail & Related papers  (2024-11-06T06:50:50Z)
• Social Debiasing for Fair Multi-modal LLMs [59.61512883471714]
  Multi-modal Large Language Models (MLLMs) have dramatically advanced the research field and delivered powerful vision-language understanding capabilities.<n>These models often inherit deep-rooted social biases from their training data, leading to uncomfortable responses with respect to attributes such as race and gender.<n>This paper addresses the issue of social biases in MLLMs by introducing a comprehensive counterfactual dataset with multiple social concepts.
  arXiv  Detail & Related papers  (2024-08-13T02:08:32Z)
• Measuring Implicit Bias in Explicitly Unbiased Large Language Models [14.279977138893846]
  Large language models (LLMs) can pass explicit social bias tests but still harbor implicit biases. We introduce two new measures of bias: LLM Implicit Bias, a prompt-based method for revealing implicit bias; and LLM Decision Bias, a strategy to detect subtle discrimination in decision-making tasks. Using these measures, we found pervasive stereotype biases mirroring those in society in 8 value-aligned models across 4 social categories.
  arXiv  Detail & Related papers  (2024-02-06T15:59:23Z)

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.