Metamorphic Testing for Fairness Evaluation in Large Language Models: Identifying Intersectional Bias in LLaMA and GPT

• URL: http://arxiv.org/abs/2504.07982v1
• Date: Fri, 04 Apr 2025 21:04:14 GMT
• Title: Metamorphic Testing for Fairness Evaluation in Large Language Models: Identifying Intersectional Bias in LLaMA and GPT
• Authors: Harishwar Reddy, Madhusudan Srinivasan, Upulee Kanewala,
• Abstract summary: Large Language Models (LLMs) have made significant strides in Natural Language Processing but remain vulnerable to fairness-related issues.<n>This paper introduces a metamorphic testing approach to systematically identify fairness bugs in LLMs.
• Score: 2.380039717474099
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) have made significant strides in Natural Language Processing but remain vulnerable to fairness-related issues, often reflecting biases inherent in their training data. These biases pose risks, particularly when LLMs are deployed in sensitive areas such as healthcare, finance, and law. This paper introduces a metamorphic testing approach to systematically identify fairness bugs in LLMs. We define and apply a set of fairness-oriented metamorphic relations (MRs) to assess the LLaMA and GPT model, a state-of-the-art LLM, across diverse demographic inputs. Our methodology includes generating source and follow-up test cases for each MR and analyzing model responses for fairness violations. The results demonstrate the effectiveness of MT in exposing bias patterns, especially in relation to tone and sentiment, and highlight specific intersections of sensitive attributes that frequently reveal fairness faults. This research improves fairness testing in LLMs, providing a structured approach to detect and mitigate biases and improve model robustness in fairness-sensitive applications.

Related papers

• The Other Side of the Coin: Exploring Fairness in Retrieval-Augmented Generation [73.16564415490113]
  Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by retrieving relevant document from external knowledge sources. We propose two approaches, FairFT and FairFilter, to mitigate the fairness issues introduced by RAG for small-scale LLMs.
  arXiv  Detail & Related papers  (2025-04-11T10:17:10Z)
• No LLM is Free From Bias: A Comprehensive Study of Bias Evaluation in Large Language models [0.9620910657090186]
  Large Language Models (LLMs) have increased the performance of different natural language understanding as well as generation tasks.<n>Although LLMs have breached the state-of-the-art performance in various tasks, they often reflect different forms of bias present in the training data.<n>We provide a unified evaluation of benchmarks using a set of representative LLMs that cover different forms of biases starting from physical characteristics to socio-economic categories.
  arXiv  Detail & Related papers  (2025-03-15T03:58:14Z)
• Preference Leakage: A Contamination Problem in LLM-as-a-judge [69.96778498636071]
  Large Language Models (LLMs) as judges and LLM-based data synthesis have emerged as two fundamental LLM-driven data annotation methods.<n>In this work, we expose preference leakage, a contamination problem in LLM-as-a-judge caused by the relatedness between the synthetic data generators and LLM-based evaluators.
  arXiv  Detail & Related papers  (2025-02-03T17:13:03Z)
• Bias in Large Language Models: Origin, Evaluation, and Mitigation [4.606140332500086]
  Large Language Models (LLMs) have revolutionized natural language processing, but their susceptibility to biases poses significant challenges. This comprehensive review examines the landscape of bias in LLMs, from its origins to current mitigation strategies. Ethical and legal implications of biased LLMs are discussed, emphasizing potential harms in real-world applications such as healthcare and criminal justice.
  arXiv  Detail & Related papers  (2024-11-16T23:54:53Z)
• Beyond Binary: Towards Fine-Grained LLM-Generated Text Detection via Role Recognition and Involvement Measurement [51.601916604301685]
  Large language models (LLMs) generate content that can undermine trust in online discourse.<n>Current methods often focus on binary classification, failing to address the complexities of real-world scenarios like human-LLM collaboration.<n>To move beyond binary classification and address these challenges, we propose a new paradigm for detecting LLM-generated content.
  arXiv  Detail & Related papers  (2024-10-18T08:14:10Z)
• Justice or Prejudice? Quantifying Biases in LLM-as-a-Judge [84.34545223897578]
  Despite their excellence in many domains, potential issues are under-explored, undermining their reliability and the scope of their utility. We identify 12 key potential biases and propose a new automated bias quantification framework-CALM- which quantifies and analyzes each type of bias in LLM-as-a-Judge. Our work highlights the need for stakeholders to address these issues and remind users to exercise caution in LLM-as-a-Judge applications.
  arXiv  Detail & Related papers  (2024-10-03T17:53:30Z)
• Fairness in Large Language Models in Three Hours [2.443957114877221]
  This tutorial provides a systematic overview of recent advances in the literature concerning large language models. The concept of fairness in LLMs is then explored, summarizing the strategies for evaluating bias and the algorithms designed to promote fairness.
  arXiv  Detail & Related papers  (2024-08-02T03:44:14Z)
• Interpreting Bias in Large Language Models: A Feature-Based Approach [0.0]
  Large Language Models (LLMs) have showcased remarkable performance across various natural language processing (NLP) tasks. This paper investigates the propagation of biases within LLMs through a novel feature-based analytical approach.
  arXiv  Detail & Related papers  (2024-06-18T07:28:15Z)
• PoLLMgraph: Unraveling Hallucinations in Large Language Models via State Transition Dynamics [51.17512229589]
  PoLLMgraph is a model-based white-box detection and forecasting approach for large language models. We show that hallucination can be effectively detected by analyzing the LLM's internal state transition dynamics. Our work paves a new way for model-based white-box analysis of LLMs, motivating the research community to further explore, understand, and refine the intricate dynamics of LLM behaviors.
  arXiv  Detail & Related papers  (2024-04-06T20:02:20Z)
• Few-Shot Fairness: Unveiling LLM's Potential for Fairness-Aware Classification [7.696798306913988]
  We introduce a framework outlining fairness regulations aligned with various fairness definitions. We explore the configuration for in-context learning and the procedure for selecting in-context demonstrations using RAG. Experiments conducted with different LLMs indicate that GPT-4 delivers superior results in terms of both accuracy and fairness compared to other models.
  arXiv  Detail & Related papers  (2024-02-28T17:29:27Z)
• 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)

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.