mlsanityData to 7 Oct 2026

Rethinking Prompt-based Debiasing in Large Language Models

arXiv 2503.09219 ↗First posted 12 Mar 2025cs.CLcited by 4

On the map: LLM reasoning → Reasoning benchmarks and evaluation

Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning. Builds on 11 of its 40 references that are in the map. First posted on arXiv 12 Mar 2025.

Builds on

11 of 40 references.

References · 11

  1. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  2. 2023Mistral 7Bcited by 3,065
  3. 2023Universal and Transferable Adversarial Attacks on Aligned Language Modelscited by 2,914
  4. 2023The Capacity for Moral Self-Correction in Large Language Modelscited by 171
  5. 2023Evaluating and Mitigating Discrimination in Language Model Decisionscited by 117
  6. 2024Self-Debiasing Large Language Models: Zero-Shot Recognition and Reduction of Stereotypescited by 58
  7. 2024"You Gotta be a Doctor, Lin": An Investigation of Name-Based Bias of Large Language Models in Employment Recommendationscited by 33
  8. 2024Do Large Language Models Discriminate in Hiring Decisions on the Basis of Race, Ethnicity, and Gender?cited by 24
  9. 2024Context Steering: Controllable Personalization at Inference Timecited by 22
  10. 2024On the Intrinsic Self-Correction Capability of LLMs: Uncertainty and Latent Conceptcited by 16
  11. 2024MONAL: Model Autophagy Analysis for Modeling Human-AI Interactionscited by 7

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 3; 2026: 1.

4 papers.

  1. 2025Reasoning Towards Fairness: Mitigating Bias in Language Models through Reasoning-Guided Fine-Tuningcited by 5
  2. 2025Enhancing LLMs' Reasoning-Intensive Multimedia Search Capabilities through Fine-Tuning and Reinforcement Learningcited by 3
  3. 2025Bridging Fairness and Explainability: Can Input-Based Explanations Promote Fairness in Hate Speech Detection?cited by 0
  4. 2026Mechanics of Bias and Reasoning: Interpreting the Impact of Chain-of-Thought Prompting on Gender Bias in LLMscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Evaluating Nuanced Bias in Large Language Model Free Response Answers
  2. 2025Assessing Agentic Large Language Models in Multilingual National Bias
  3. 2025Bias after Prompting: Persistent Discrimination in Large Language Models
  4. 2025Single LLM Debate, MoLaCE: Mixture of Latent Concept Experts Against Confirmation Bias
  5. 2024Thinking Fair and Slow: On the Efficacy of Structured Prompts for Debiasing Language Models
  6. 2025Debiasing LLMs by Masking Unfairness-Driving Attention Heads
  7. 2024A Multi-LLM Debiasing Framework
  8. 2025Assessing Judging Bias in Large Reasoning Models: An Empirical Study