No LLM is Free From Bias: A Comprehensive Study of Bias Evaluation in Large Language models

• URL: http://arxiv.org/abs/2503.11985v1
• Date: Sat, 15 Mar 2025 03:58:14 GMT
• Title: No LLM is Free From Bias: A Comprehensive Study of Bias Evaluation in Large Language models
• Authors: Charaka Vinayak Kumar, Ashok Urlana, Gopichand Kanumolu, Bala Mallikarjunarao Garlapati, Pruthwik Mishra,
• Abstract summary: 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.
• Score: 0.9620910657090186
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Advancements in Large Language Models (LLMs) have increased the performance of different natural language understanding as well as generation tasks. 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. In the light of this perceived limitation, 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. Moreover, we propose five prompting approaches to carry out the bias detection task across different aspects of bias. Further, we formulate three research questions to gain valuable insight in detecting biases in LLMs using different approaches and evaluation metrics across benchmarks. The results indicate that each of the selected LLMs suffer from one or the other form of bias with the LLaMA3.1-8B model being the least biased. Finally, we conclude the paper with the identification of key challenges and possible future directions.

Related papers

• Implicit Bias in LLMs: A Survey [2.07180164747172]
  This paper provides a comprehensive review of the existing literature on implicit bias in Large language models. We begin by introducing key concepts, theories and methods related to implicit bias in psychology. We categorize detection methods into three primary approaches: word association, task-oriented text generation and decision-making.
  arXiv  Detail & Related papers  (2025-03-04T16:49:37Z)
• What Makes In-context Learning Effective for Mathematical Reasoning: A Theoretical Analysis [81.15503859645149]
  In this paper, we aim to theoretically analyze the impact of in-context demonstrations on large language models' reasoning performance.<n>We propose a straightforward, generalizable, and low-complexity demonstration selection method named LMS3.
  arXiv  Detail & Related papers  (2024-12-11T11:38:11Z)
• Different Bias Under Different Criteria: Assessing Bias in LLMs with a Fact-Based Approach [7.969162168078149]
  Large language models (LLMs) often reflect real-world biases, leading to efforts to mitigate these effects. We introduce a novel metric to assess bias using fact-based criteria and real-world statistics.
  arXiv  Detail & Related papers  (2024-11-26T11:32:43Z)
• 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)
• Examining the Influence of Political Bias on Large Language Model Performance in Stance Classification [5.8229466650067065]
  We investigate whether large language models (LLMs) exhibit a tendency to more accurately classify politically-charged stances. Our findings reveal a statistically significant difference in the performance of LLMs across various politically oriented stance classification tasks. LLMs have poorer stance classification accuracy when there is greater ambiguity in the target the statement is directed towards.
  arXiv  Detail & Related papers  (2024-07-25T01:11:38Z)
• CEB: Compositional Evaluation Benchmark for Fairness in Large Language Models [58.57987316300529]
  Large Language Models (LLMs) are increasingly deployed to handle various natural language processing (NLP) tasks.<n>To evaluate the biases exhibited by LLMs, researchers have recently proposed a variety of datasets.<n>We propose CEB, a Compositional Evaluation Benchmark that covers different types of bias across different social groups and tasks.
  arXiv  Detail & Related papers  (2024-07-02T16:31:37Z)
• 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)
• Debiasing Multimodal Large Language Models [61.6896704217147]
  Large Vision-Language Models (LVLMs) have become indispensable tools in computer vision and natural language processing. Our investigation reveals a noteworthy bias in the generated content, where the output is primarily influenced by the underlying Large Language Models (LLMs) prior to the input image. To rectify these biases and redirect the model's focus toward vision information, we introduce two simple, training-free strategies.
  arXiv  Detail & Related papers  (2024-03-08T12:35:07Z)
• Large Language Model (LLM) Bias Index -- LLMBI [0.0]
  The Large Language Model Bias Index (LLMBI) is a pioneering approach designed to quantify and address biases inherent in large language models (LLMs) We formulated LLMBI using a composite scoring system incorporating multiple dimensions of bias, including but not limited to age, gender, and racial biases. Our empirical analysis, conducted using responses from OpenAI's API, employs advanced sentiment analysis as a representative method for bias detection.
  arXiv  Detail & Related papers  (2023-12-22T15:38:13Z)
• 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 and Fairness in Large Language Models: A Survey [73.87651986156006]
  We present a comprehensive survey of bias evaluation and mitigation techniques for large language models (LLMs) We first consolidate, formalize, and expand notions of social bias and fairness in natural language processing. We then unify the literature by proposing three intuitive, two for bias evaluation, and one for mitigation.
  arXiv  Detail & Related papers  (2023-09-02T00:32:55Z)
• Causally Testing Gender Bias in LLMs: A Case Study on Occupational Bias [33.99768156365231]
  We introduce a causal formulation for bias measurement in generative language models. We propose a benchmark called OccuGender, with a bias-measuring procedure to investigate occupational gender bias. The results show that these models exhibit substantial occupational gender bias.
  arXiv  Detail & Related papers  (2022-12-20T22:41:24Z)

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.