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Rethinking Prompt-based Debiasing in Large Language Models

Xinyi Yang, Runzhe Zhan, Derek F. Wong, Shu Yang, Junchao Wu, Lidia S. Chao

TL;DR

The paper questions the reliability of prompt-based debiasing in LLMs by separating true bias understanding from superficial pattern matching through self-diagnosis and debiasing analyses on BBQ and StereoSet across open-source and commercial models. It shows that bias detection is context-dependent and that debiasing prompts often yield evasive responses, while standard metrics can misrepresent progress. The findings reveal fragile, inconsistent improvements and a potential false prosperity in current debiasing approaches, urging a rethink of bias metrics and benchmarking. The work highlights the need for robust evaluation frameworks that balance bias mitigation with preserved reasoning and language modeling capabilities in AI systems.

Abstract

Investigating bias in large language models (LLMs) is crucial for developing trustworthy AI. While prompt-based through prompt engineering is common, its effectiveness relies on the assumption that models inherently understand biases. Our study systematically analyzed this assumption using the BBQ and StereoSet benchmarks on both open-source models as well as commercial GPT model. Experimental results indicate that prompt-based is often superficial; for instance, the Llama2-7B-Chat model misclassified over 90% of unbiased content as biased, despite achieving high accuracy in identifying bias issues on the BBQ dataset. Additionally, specific evaluation and question settings in bias benchmarks often lead LLMs to choose "evasive answers", disregarding the core of the question and the relevance of the response to the context. Moreover, the apparent success of previous methods may stem from flawed evaluation metrics. Our research highlights a potential "false prosperity" in prompt-base efforts and emphasizes the need to rethink bias metrics to ensure truly trustworthy AI.

Rethinking Prompt-based Debiasing in Large Language Models

TL;DR

The paper questions the reliability of prompt-based debiasing in LLMs by separating true bias understanding from superficial pattern matching through self-diagnosis and debiasing analyses on BBQ and StereoSet across open-source and commercial models. It shows that bias detection is context-dependent and that debiasing prompts often yield evasive responses, while standard metrics can misrepresent progress. The findings reveal fragile, inconsistent improvements and a potential false prosperity in current debiasing approaches, urging a rethink of bias metrics and benchmarking. The work highlights the need for robust evaluation frameworks that balance bias mitigation with preserved reasoning and language modeling capabilities in AI systems.

Abstract

Investigating bias in large language models (LLMs) is crucial for developing trustworthy AI. While prompt-based through prompt engineering is common, its effectiveness relies on the assumption that models inherently understand biases. Our study systematically analyzed this assumption using the BBQ and StereoSet benchmarks on both open-source models as well as commercial GPT model. Experimental results indicate that prompt-based is often superficial; for instance, the Llama2-7B-Chat model misclassified over 90% of unbiased content as biased, despite achieving high accuracy in identifying bias issues on the BBQ dataset. Additionally, specific evaluation and question settings in bias benchmarks often lead LLMs to choose "evasive answers", disregarding the core of the question and the relevance of the response to the context. Moreover, the apparent success of previous methods may stem from flawed evaluation metrics. Our research highlights a potential "false prosperity" in prompt-base efforts and emphasizes the need to rethink bias metrics to ensure truly trustworthy AI.

Paper Structure

This paper contains 35 sections, 4 equations, 4 figures, 10 tables.

Table of Contents

  1. Introduction
  2. Background
  3. Social Bias in LLMs
  4. Prompt-based Debiasing in LLMs
  5. Analytical Methodology
  6. Self-Diagnosis
  7. Prompt-based Debiasing Methods
  8. Experiments
  9. Data and Evaluation
  10. The Bias Benchmark for Q&A (BBQ)
  11. Evaluation
  12. StereoSet Dataset
  13. Evaluation
  14. Experimental Setting
  15. Results and Analysis
  16. ...and 20 more sections

Figures (4)

  • Figure 1: The overall framework for evaluating LLMs’ bias understanding and mitigation includes self-diagnosis tasks and prompt-based debiasing methods. The BBQ dataset is used as an illustration.
  • Figure 2: The experimental results from the self-diagnosis task conducted on the BBQ dataset. Region shows the probability of answering "Yes".
  • Figure 3: Comparison of model consistency in prompt-base with CoT and non-debiasing baseline on the BBQ dataset. Document-level accuracy "Acc" indicates the proportion of instances where the correct answer holds the highest probability. Option-level analysis examines the average proportion for three options. "Unk" and "Wro" denote Unknown and Wrong options.
  • Figure :