mlsanityData to 14 Oct 2026

A Multi-LLM Debiasing Framework

arXiv 2409.13884 ↗First posted 20 Sep 2024cs.CL cs.AI cs.CYcited by 10

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Abstract (arXiv)

Large Language Models (LLMs) are powerful tools with the potential to benefit society immensely, yet, they have demonstrated biases that perpetuate societal inequalities. Despite significant advancements in bias mitigation techniques using data augmentation, zero-shot prompting, and model fine-tuning, biases continuously persist, including subtle biases that may elude human detection. Recent research has shown a growing interest in multi-LLM approaches, which have been demonstrated to be effective in improving the quality of reasoning and factuality in LLMs. Building on this approach, we propose a novel multi-LLM debiasing framework aimed at reducing bias in LLMs. Our work is the first to introduce and evaluate two distinct approaches within this framework for debiasing LLMs: a centralized method, where the conversation is facilitated by a single central LLM, and a decentralized method, where all models communicate directly. Our findings reveal that our multi-LLM framework significantly reduces bias in LLMs, outperforming the baseline method across several social groups.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 21 of its 55 references that are in the map. First posted on arXiv 20 Sep 2024.

Builds on

21 of 55 references.

References · 21

  1. 2023ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debatecited by 742
  2. 2021BBQ: A Hand-Built Bias Benchmark for Question Answeringcited by 701
  3. 2024Prometheus 2: An Open Source Language Model Specialized in Evaluating Other Language Modelscited by 446
  4. 2021Towards Understanding and Mitigating Social Biases in Language Modelscited by 296
  5. 2024Rethinking the Bounds of LLM Reasoning: Are Multi-Agent Discussions the Key?cited by 159
  6. 2024AutoDefense: Multi-Agent LLM Defense against Jailbreak Attackscited by 145
  7. 2024Improving Multi-Agent Debate with Sparse Communication Topologycited by 107
  8. 2024Benchmark Self-Evolving: A Multi-Agent Framework for Dynamic LLM Evaluationcited by 75
  9. 2024Evaluating Gender Bias in Large Language Models via Chain-of-Thought Promptingcited by 50
  10. 2022Mitigating Gender Bias in Distilled Language Models via Counterfactual Role Reversalcited by 40
  11. 2024LLM-Ensemble: Optimal Large Language Model Ensemble Method for E-commerce Product Attribute Value Extractioncited by 39
  12. 2024Causal Prompting: Debiasing Large Language Model Prompting based on Front-Door Adjustmentcited by 30
9 more
  1. 2023PREFER: Prompt Ensemble Learning via Feedback-Reflect-Refinecited by 28
  2. 2022A Robust Bias Mitigation Procedure Based on the Stereotype Content Modelcited by 24
  3. 2022Gender Biases and Where to Find Them: Exploring Gender Bias in Pre-Trained Transformer-based Language Models Using Movement Pruningcited by 21
  4. 2024ChatGPT Based Data Augmentation for Improved Parameter-Efficient Debiasing of LLMscited by 18
  5. 2024Few-Shot Fairness: Unveiling LLM's Potential for Fairness-Aware Classificationcited by 18
  6. 2024AXOLOTL: Fairness through Assisted Self-Debiasing of Large Language Model Outputscited by 7
  7. 2024Position-Aware Parameter Efficient Fine-Tuning Approach for Reducing Positional Bias in LLMscited by 7
  8. 2022Does Debiasing Inevitably Degrade the Model Performancecited by 2
  9. 2024Large, Small or Both: A Novel Data Augmentation Framework Based on Language Models for Debiasing Opinion Summarizationcited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 4; 2026: 5.

10 papers.

  1. 2024LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methodscited by 465
  2. 2025LLM Post-Training: A Deep Dive into Reasoning Large Language Modelscited by 135
  3. 2026Large Language Model Reasoning Failurescited by 21
  4. 2025A Survey of Scaling in Large Language Model Reasoningcited by 10
  5. 2026CatRAG: Functor-Guided Structural Debiasing with Retrieval Augmentation for Fair LLMscited by 9
  6. 2025Scoring, Reasoning, and Selecting the Best! Ensembling Large Language Models via a Peer-Review Processcited by 4
  7. 2026Completing Missing Annotation: Multi-Agent Debate for Accurate and Scalable Relevant Assessment for IR Benchmarkscited by 1
  8. 2025A Trustworthy Multi-LLM Network: Challenges,Solutions, and A Use Casecited by 0
  9. 2026Analysis Of Linguistic Stereotypes in Single and Multi-Agent Generative AI Architecturescited by 0
  10. 2026Collective Bias Mitigation via Model Routing and Collaborationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Towards Resource Efficient and Interpretable Bias Mitigation in Large Language Models
  2. 2025BiasFilter: An Inference-Time Debiasing Framework for Large Language Models
  3. 2024With a Grain of SALT: Are LLMs Fair Across Social Dimensions?
  4. 2025Debiasing LLMs by Masking Unfairness-Driving Attention Heads
  5. 2024Interpreting Bias in Large Language Models: A Feature-Based Approach
  6. 2025Do LLMs Align Human Values Regarding Social Biases? Judging and Explaining Social Biases with LLMs
  7. 2024BiasAlert: A Plug-and-play Tool for Social Bias Detection in LLMs
  8. 2025CoBia: Constructed Conversations Can Trigger Otherwise Concealed Societal Biases in LLMs