mlsanityData to 7 Oct 2026

Few-Shot Fairness: Unveiling LLM's Potential for Fairness-Aware Classification

arXiv 2402.18502 ↗First posted 28 Feb 2024cs.CLcited by 18

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Abstract (arXiv)

Employing Large Language Models (LLM) in various downstream applications such as classification is crucial, especially for smaller companies lacking the expertise and resources required for fine-tuning a model. Fairness in LLMs helps ensure inclusivity, equal representation based on factors such as race, gender and promotes responsible AI deployment. As the use of LLMs has become increasingly prevalent, it is essential to assess whether LLMs can generate fair outcomes when subjected to considerations of fairness. In this study, we introduce a framework outlining fairness regulations aligned with various fairness definitions, with each definition being modulated by varying degrees of abstraction. We explore the configuration for in-context learning and the procedure for selecting in-context demonstrations using RAG, while incorporating fairness rules into the process. Experiments conducted with different LLMs indicate that GPT-4 delivers superior results in terms of both accuracy and fairness compared to other models. This work is one of the early attempts to achieve fairness in prediction tasks by utilizing LLMs through in-context learning.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). Builds on 33 of its 44 references that are in the map. First posted on arXiv 28 Feb 2024.

Builds on

33 of 44 references.

References · 33

  1. 2017Attention Is All You Needcited by 59,932
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2023GPT-4 Technical Reportcited by 18,132
  4. 2022Training language models to follow instructions with human feedbackcited by 15,712
  5. 2017Deep reinforcement learning from human preferencescited by 3,846
  6. 2022Constitutional AI: Harmlessness from AI Feedbackcited by 2,950
  7. 2016Equality of Opportunity in Supervised Learningcited by 2,000
  8. 2021Calibrate Before Use: Improving Few-Shot Performance of Language Modelscited by 1,371
  9. 2021Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivitycited by 1,122
  10. 2021A General Language Assistant as a Laboratory for Alignmentcited by 1,023
  11. 2022Red Teaming Language Models with Language Modelscited by 983
  12. 2020Language (Technology) is Power: A Critical Survey of "Bias" in NLPcited by 951
21 more
  1. 2020StereoSet: Measuring stereotypical bias in pretrained language modelscited by 911
  2. 2017Counterfactual Fairnesscited by 802
  3. 2014Certifying and removing disparate impactcited by 715
  4. 2020CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Modelscited by 703
  5. 2023Large Language Models Are Not Robust Multiple Choice Selectorscited by 454
  6. 2017On Fairness and Calibrationcited by 406
  7. 2017Algorithmic decision making and the cost of fairnesscited by 402
  8. 2017Fairness in Criminal Justice Risk Assessments: The State of the Artcited by 315
  9. 2019Identifying and Reducing Gender Bias in Word-Level Language Modelscited by 214
  10. 2022Predictability and Surprise in Large Generative Modelscited by 209
  11. 2020Social Biases in NLP Models as Barriers for Persons with Disabilitiescited by 182
  12. 2023Should ChatGPT be Biased? Challenges and Risks of Bias in Large Language Modelscited by 144
  13. 2017Fairness Testing: Testing Software for Discriminationcited by 123
  14. 2023Is ChatGPT Fair for Recommendation? Evaluating Fairness in Large Language Model Recommendationcited by 113
  15. 2023Finding Support Examples for In-Context Learningcited by 104
  16. 2019Evaluating the Underlying Gender Bias in Contextualized Word Embeddingscited by 94
  17. 2022Language Generation Models Can Cause Harm: So What Can We Do About It? An Actionable Surveycited by 80
  18. 2023Ethical Reasoning over Moral Alignment: A Case and Framework for In-Context Ethical Policies in LLMscited by 64
  19. 2023Bias Testing and Mitigation in LLM-based Code Generationcited by 53
  20. 2023Confronting LLMs with Traditional ML: Rethinking the Fairness of Large Language Models in Tabular Classificationscited by 15
  21. 2023A Group Fairness Lens for Large Language Modelscited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 9; 2025: 8; 2026: 1.

18 papers.

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  1. 2024Large Language Models for Education: A Survey and Outlookcited by 185
  2. 2024A Survey on Medical Large Language Models: Technology, Application, Trustworthiness, and Future Directionscited by 34
  3. 2024Mitigating the Risk of Health Inequity Exacerbated by Large Language Modelscited by 21
  4. 2025Population-Aligned Persona Generation for LLM-based Social Simulationcited by 20
  5. 2024Fairness Definitions in Language Models Explainedcited by 10
  6. 2024A Multi-LLM Debiasing Frameworkcited by 9
  7. 2025Are LLMs Empathetic to All? Investigating the Influence of Multi-Demographic Personas on a Model's Empathycited by 9
  8. 2024Evaluating and Mitigating Social Bias for Large Language Models in Open-ended Settingscited by 8
  9. 2024Fairness in Large Language Models in Three Hourscited by 7
  10. 2024Fair In-Context Learning via Latent Concept Variablescited by 3
  11. 2024LaMSUM: Amplifying Voices Against Harassment through LLM Guided Extractive Summarization of User Incident Reportscited by 2
  12. 2025MARRO: Multi-headed Attention for Rhetorical Role Labeling in Legal Documentscited by 2
6 more
  1. 2025Fairness in Augmented Graph Learning: A Surveycited by 2
  2. 2025It's complicated. The relationship of algorithmic fairness and non-discrimination provisions for high-risk systems in the EU AI Actcited by 0
  3. 2025Can ChatGPT Diagnose Alzheimer's Disease?cited by 0
  4. 2025SMITE: Enhancing Fairness in LLMs through Optimal In-Context Example Selection via Dynamic Validationcited by 0
  5. 2025Gender Bias in Emotion Recognition by Large Language Modelscited by 0
  6. 2026Mind the Gap: Pitfalls of LLM Alignment with Asian Public Opinioncited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Fair Like Us? Auditing LLM Alignment in Resource Allocation
  2. 2025FairCoder: Probing LLM Bias in High-Stakes Decision Making via Coding Tasks
  3. 2025Towards Large Language Models that Benefit for All: Benchmarking Group Fairness in Reward Models
  4. 2026Intra-Fairness Dynamics: The Bias Spillover Effect in Targeted LLM Alignment
  5. 2025Guiding LLM Decision-Making with Fairness Reward Models
  6. 2024Improving LLM Group Fairness on Tabular Data via In-Context Learning
  7. 2025Silenced Biases: The Dark Side LLMs Learned to Refuse
  8. 2025BiasGuard: A Reasoning-enhanced Bias Detection Tool For Large Language Models