mlsanityData to 14 Oct 2026

DR.GAP: Mitigating Bias in Large Language Models using Gender-Aware Prompting with Decoupled Reasoning

arXiv 2502.11603 ↗First posted 17 Feb 2025cs.CL cs.AIcited by 2

On the map: LLM reasoning → Reasoning benchmarks and evaluation

Abstract (arXiv)

Large Language Models (LLMs) exhibit strong natural language understanding capabilities but also inherit and amplify societal biases, particularly gender bias, raising fairness concerns. Existing prompt-based debiasing strategies share a key limitation: they fail to disentangle gender information from task semantics. Bias steering compels models to overemphasize gender cues, while reasoning-based prompting induces gender-biased reasoning chains. To address these challenges, we propose DR.GAP (Decoupled Reasoning for Gender-Aware Prompting), an automated and model-agnostic pipeline that mitigates gender bias while preserving model performance. DR.GAP generates gender-neutral reasoning traces and applies them as in-context demonstrations during inference, effectively decoupling gender attributes from task semantics without modifying model parameters. Extensive experiments on coreference resolution and question-answering tasks across six LLMs demonstrate DR.GAP's effectiveness, generalizability, and robustness, supported by detailed mechanism analyses. Moreover, DR.GAP can be extended to vision-language models (VLMs), achieving substantial bias reduction.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning. Builds on 8 of its 37 references that are in the map. First posted on arXiv 17 Feb 2025.

Builds on

8 of 37 references.

References · 8

  1. 2023GPT-4 Technical Reportcited by 18,396
  2. 2024The Llama 3 Herd of Modelscited by 14,715
  3. 2020Measuring Massive Multitask Language Understandingcited by 7,963
  4. 2024GPT-4o System Cardcited by 4,588
  5. 2024Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolutioncited by 4,234
  6. 2023The Capacity for Moral Self-Correction in Large Language Modelscited by 173
  7. 2022Text and Patterns: For Effective Chain of Thought, It Takes Two to Tangocited by 128
  8. 2024Decoding Biases: Automated Methods and LLM Judges for Gender Bias Detection in Language Modelscited by 33

Built on by

Cited by year (2026 to 14 Oct)

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

2 papers.

  1. 2025Reasoning Towards Fairness: Mitigating Bias in Language Models through Reasoning-Guided Fine-Tuningcited by 5
  2. 2026Mitigating Prompt-Induced Cognitive Biases in General-Purpose AI for Software Engineeringcited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2024Think Before You Act: A Two-Stage Framework for Mitigating Gender Bias Towards Vision-Language Tasks
  2. 2026Mechanics of Bias and Reasoning: Interpreting the Impact of Chain-of-Thought Prompting on Gender Bias in LLMs
  3. 2026Gender Bias in Vision-Language In-Context Learning
  4. 2024Disclosure and Mitigation of Gender Bias in LLMs
  5. 2024Evaluating Gender Bias in Large Language Models via Chain-of-Thought Prompting
  6. 2026Alignment Reduces Expressed but Not Encoded Gender Bias: A Unified Framework and Study
  7. 2026ViD: Vision-Dominant Gender Bias Mitigation for Large Vision-Language Models
  8. 2023Learning from Red Teaming: Gender Bias Provocation and Mitigation in Large Language Models