Decoding Biases: Automated Methods and LLM Judges for Gender Bias Detection in Language Models

• URL: http://arxiv.org/abs/2408.03907v1
• Date: Wed, 7 Aug 2024 17:11:34 GMT
• Title: Decoding Biases: Automated Methods and LLM Judges for Gender Bias Detection in Language Models
• Authors: Shachi H Kumar, Saurav Sahay, Sahisnu Mazumder, Eda Okur, Ramesh Manuvinakurike, Nicole Beckage, Hsuan Su, Hung-yi Lee, Lama Nachman,
• Abstract summary: Large Language Models (LLMs) have excelled at language understanding and generating human-level text. LLMs are susceptible to adversarial attacks where malicious users prompt the model to generate undesirable text. In this work, we train models to automatically create adversarial prompts to elicit biased responses from target LLMs.
• Score: 47.545382591646565
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) have excelled at language understanding and generating human-level text. However, even with supervised training and human alignment, these LLMs are susceptible to adversarial attacks where malicious users can prompt the model to generate undesirable text. LLMs also inherently encode potential biases that can cause various harmful effects during interactions. Bias evaluation metrics lack standards as well as consensus and existing methods often rely on human-generated templates and annotations which are expensive and labor intensive. In this work, we train models to automatically create adversarial prompts to elicit biased responses from target LLMs. We present LLM- based bias evaluation metrics and also analyze several existing automatic evaluation methods and metrics. We analyze the various nuances of model responses, identify the strengths and weaknesses of model families, and assess where evaluation methods fall short. We compare these metrics to human evaluation and validate that the LLM-as-a-Judge metric aligns with human judgement on bias in response generation.

Related papers

• Towards Understanding the Robustness of LLM-based Evaluations under Perturbations [9.944512689015998]
  Large Language Models (LLMs) can serve as automatic evaluators for non-standardized metrics in summarization and dialog-based tasks. We conduct experiments across multiple prompting strategies to examine how LLMs fare as quality evaluators when compared with human judgments.
  arXiv  Detail & Related papers  (2024-12-12T13:31:58Z)
• Sample-Efficient Human Evaluation of Large Language Models via Maximum Discrepancy Competition [46.949604465227054]
  We propose a sample-efficient human evaluation method based on MAximum Discrepancy (MAD) competition. MAD automatically selects a small set of informative and diverse instructions, each adapted to two LLMs. The pairwise comparison results are then aggregated into a global ranking using the Elo rating system.
  arXiv  Detail & Related papers  (2024-04-10T01:26:24Z)
• Likelihood-based Mitigation of Evaluation Bias in Large Language Models [37.07596663793111]
  Large Language Models (LLMs) are widely used to evaluate natural language generation tasks as automated metrics. It is possible that there might be a likelihood bias if LLMs are used for evaluation.
  arXiv  Detail & Related papers  (2024-02-25T04:52:02Z)
• Measuring Social Biases in Masked Language Models by Proxy of Prediction Quality [0.0]
  Transformer models have achieved state-of-the-art performance for a variety of natural language tasks but have been shown to encode unwanted biases. We evaluate the social biases encoded by transformers trained with the masked language modeling objective using proposed proxy functions to measure the quality of transformer models' predictions. We find all models encode concerning social biases. We compare bias estimations with those produced by other evaluation methods using benchmark datasets.
  arXiv  Detail & Related papers  (2024-02-21T17:33:13Z)
• How Reliable Are Automatic Evaluation Methods for Instruction-Tuned LLMs? [3.1706553206969925]
  We perform a meta-evaluation of such methods and assess their reliability across a broad range of tasks. We observe that while automatic evaluation methods can approximate human ratings under specific conditions, their validity is highly context-dependent. Our findings enhance the understanding of how automatic methods should be applied and interpreted when developing and evaluating instruction-tuned LLMs.
  arXiv  Detail & Related papers  (2024-02-16T15:48:33Z)
• CLOMO: Counterfactual Logical Modification with Large Language Models [109.60793869938534]
  We introduce a novel task, Counterfactual Logical Modification (CLOMO), and a high-quality human-annotated benchmark. In this task, LLMs must adeptly alter a given argumentative text to uphold a predetermined logical relationship. We propose an innovative evaluation metric, the Self-Evaluation Score (SES), to directly evaluate the natural language output of LLMs.
  arXiv  Detail & Related papers  (2023-11-29T08:29:54Z)
• Exploring the Reliability of Large Language Models as Customized Evaluators for Diverse NLP Tasks [65.69651759036535]
  We analyze whether large language models (LLMs) can serve as reliable alternatives to humans. This paper explores both conventional tasks (e.g., story generation) and alignment tasks (e.g., math reasoning) We find that LLM evaluators can generate unnecessary criteria or omit crucial criteria, resulting in a slight deviation from the experts.
  arXiv  Detail & Related papers  (2023-10-30T17:04:35Z)
• Bring Your Own Data! Self-Supervised Evaluation for Large Language Models [52.15056231665816]
  We propose a framework for self-supervised evaluation of Large Language Models (LLMs) We demonstrate self-supervised evaluation strategies for measuring closed-book knowledge, toxicity, and long-range context dependence. We find strong correlations between self-supervised and human-supervised evaluations.
  arXiv  Detail & Related papers  (2023-06-23T17:59:09Z)
• Large Language Models are Not Yet Human-Level Evaluators for Abstractive Summarization [66.08074487429477]
  We investigate the stability and reliability of large language models (LLMs) as automatic evaluators for abstractive summarization. We find that while ChatGPT and GPT-4 outperform the commonly used automatic metrics, they are not ready as human replacements.
  arXiv  Detail & Related papers  (2023-05-22T14:58:13Z)
• Can Large Language Models Be an Alternative to Human Evaluations? [80.81532239566992]
  Large language models (LLMs) have demonstrated exceptional performance on unseen tasks when only the task instructions are provided. We show that the result of LLM evaluation is consistent with the results obtained by expert human evaluation.
  arXiv  Detail & Related papers  (2023-05-03T07:28:50Z)

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.