mlsanityData to 14 Oct 2026

AXOLOTL: Fairness through Assisted Self-Debiasing of Large Language Model Outputs

arXiv 2403.00198 ↗First posted 1 Mar 2024cs.CL cs.AI cs.CYcited by 7

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Abstract (arXiv)

Pre-trained Large Language Models (LLMs) have significantly advanced natural language processing capabilities but are susceptible to biases present in their training data, leading to unfair outcomes in various applications. While numerous strategies have been proposed to mitigate bias, they often require extensive computational resources and may compromise model performance. In this work, we introduce AXOLOTL, a novel post-processing framework, which operates agnostically across tasks and models, leveraging public APIs to interact with LLMs without direct access to internal parameters. Through a three-step process resembling zero-shot learning, AXOLOTL identifies biases, proposes resolutions, and guides the model to self-debias its outputs. This approach minimizes computational costs and preserves model performance, making AXOLOTL a promising tool for debiasing LLM outputs with broad applicability and ease of use.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 17 of its 30 references that are in the map. First posted on arXiv 1 Mar 2024.

Builds on

17 of 30 references.

References · 17

  1. 2016Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddingscited by 1,312
  2. 2020StereoSet: Measuring stereotypical bias in pretrained language modelscited by 924
  3. 2016Semantics derived automatically from language corpora contain human-like biasescited by 901
  4. 2019DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generationcited by 842
  5. 2018Gender Bias in Coreference Resolution: Evaluation and Debiasing Methodscited by 659
  6. 2019The Woman Worked as a Babysitter: On Biases in Language Generationcited by 541
  7. 2019Release Strategies and the Social Impacts of Language Modelscited by 513
  8. 2020Null It Out: Guarding Protected Attributes by Iterative Nullspace Projectioncited by 455
  9. 2021BOLD: Dataset and Metrics for Measuring Biases in Open-Ended Language Generationcited by 412
  10. 2021Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLPcited by 337
  11. 2022One Embedder, Any Task: Instruction-Finetuned Text Embeddingscited by 333
  12. 2019Gender Bias in Contextualized Word Embeddingscited by 203
5 more
  1. 2021RedditBias: A Real-World Resource for Bias Evaluation and Debiasing of Conversational Language Modelscited by 149
  2. 2019It's All in the Name: Mitigating Gender Bias with Name-Based Counterfactual Data Substitutioncited by 103
  3. 2019Evaluating the Underlying Gender Bias in Contextualized Word Embeddingscited by 94
  4. 2019Does Gender Matter? Towards Fairness in Dialogue Systemscited by 89
  5. 2021FairFil: Contrastive Neural Debiasing Method for Pretrained Text Encoderscited by 75

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 4; 2025: 2; 2026: 1.

7 papers.

  1. 2024Prompting Techniques for Reducing Social Bias in LLMs through System 1 and System 2 Cognitive Processescited by 39
  2. 2024A Multi-LLM Debiasing Frameworkcited by 10
  3. 2024Evaluating and Mitigating Social Bias for Large Language Models in Open-ended Settingscited by 10
  4. 2025BiasFilter: An Inference-Time Debiasing Framework for Large Language Modelscited by 6
  5. 2025Mitigation of Gender and Ethnicity Bias in AI-Generated Stories through Model Explanationscited by 1
  6. 2024Rank It, Then Ask It: Input Reranking for Maximizing the Performance of LLMs on Symmetric Taskscited by 0
  7. 2026Acmite: Mitigating Gender Bias in LLMs through Concept-Guided Mutual Informationcited by 0