mlsanityData to 7 Oct 2026

Do Large Language Models Rank Fairly? An Empirical Study on the Fairness of LLMs as Rankers

arXiv 2404.03192 ↗First posted 4 Apr 2024cs.IR cs.CLcited by 17

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Abstract (arXiv)

The integration of Large Language Models (LLMs) in information retrieval has raised a critical reevaluation of fairness in the text-ranking models. LLMs, such as GPT models and Llama2, have shown effectiveness in natural language understanding tasks, and prior works (e.g., RankGPT) have also demonstrated that the LLMs exhibit better performance than the traditional ranking models in the ranking task. However, their fairness remains largely unexplored. This paper presents an empirical study evaluating these LLMs using the TREC Fair Ranking dataset, focusing on the representation of binary protected attributes such as gender and geographic location, which are historically underrepresented in search outcomes. Our analysis delves into how these LLMs handle queries and documents related to these attributes, aiming to uncover biases in their ranking algorithms. We assess fairness from both user and content perspectives, contributing an empirical benchmark for evaluating LLMs as the fair ranker.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). Builds on 36 of its 53 references that are in the map. First posted on arXiv 4 Apr 2024.

Builds on

36 of 53 references.

References · 36

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2022Training language models to follow instructions with human feedbackcited by 15,712
  3. 2022Constitutional AI: Harmlessness from AI Feedbackcited by 2,950
  4. 2023Sparks of Artificial General Intelligence: Early experiments with GPT-4cited by 2,579
  5. 2020RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Modelscited by 1,235
  6. 2022Red Teaming Language Models with Language Modelscited by 983
  7. 2020CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Modelscited by 703
  8. 2021BBQ: A Hand-Built Bias Benchmark for Question Answeringcited by 683
  9. 2019Passage Re-ranking with BERTcited by 629
  10. 2023DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Modelscited by 505
  11. 2021Persistent Anti-Muslim Bias in Large Language Modelscited by 376
  12. 2020Document Ranking with a Pretrained Sequence-to-Sequence Modelcited by 373
24 more
  1. 2023Large Language Models are Effective Text Rankers with Pairwise Ranking Promptingcited by 302
  2. 2023Fine-Tuning LLaMA for Multi-Stage Text Retrievalcited by 271
  3. 2023Marked Personas: Using Natural Language Prompts to Measure Stereotypes in Language Modelscited by 204
  4. 2019Multi-Stage Document Ranking with BERTcited by 200
  5. 2018Fairness of Exposure in Rankingscited by 189
  6. 2020Social Biases in NLP Models as Barriers for Persons with Disabilitiescited by 182
  7. 2022Improving Passage Retrieval with Zero-Shot Question Generationcited by 164
  8. 2019Fairness in Recommendation Ranking through Pairwise Comparisonscited by 127
  9. 2018Equity of Attention: Amortizing Individual Fairness in Rankingscited by 124
  10. 2017FA*IR: A Fair Top-k Ranking Algorithmcited by 113
  11. 2023Is ChatGPT Fair for Recommendation? Evaluating Fairness in Large Language Model Recommendationcited by 113
  12. 2023NLPositionality: Characterizing Design Biases of Datasets and Modelscited by 91
  13. 2023Beyond Yes and No: Improving Zero-Shot LLM Rankers via Scoring Fine-Grained Relevance Labelscited by 86
  14. 2016Measuring Fairness in Ranked Outputscited by 81
  15. 2021The Expando-Mono-Duo Design Pattern for Text Ranking with Pretrained Sequence-to-Sequence Modelscited by 81
  16. 2017Ranking with Fairness Constraintscited by 78
  17. 2018iFair: Learning Individually Fair Data Representations for Algorithmic Decision Makingcited by 64
  18. 2023Open-source Large Language Models are Strong Zero-shot Query Likelihood Models for Document Rankingcited by 44
  19. 2017Designing Fair Ranking Schemescited by 40
  20. 2018Reducing Disparate Exposure in Ranking: A Learning To Rank Approachcited by 33
  21. 2018Selection Problems in the Presence of Implicit Biascited by 29
  22. 2019Balanced Ranking with Diversity Constraintscited by 20
  23. 2021Text-to-Text Multi-view Learning for Passage Re-rankingcited by 14
  24. 2023Overview of the TREC 2021 Fair Ranking Trackcited by 7

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 5; 2025: 9; 2026: 3.

17 papers.

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  2. 2025Towards Trustworthy Retrieval Augmented Generation for Large Language Models: A Surveycited by 29
  3. 2024Does RAG Introduce Unfairness in LLMs? Evaluating Fairness in Retrieval-Augmented Generation Systemscited by 26
  4. 2025The Scales of Justitia: A Comprehensive Survey on Safety Evaluation of LLMscited by 24
  5. 2025Position is Power: System Prompts as a Mechanism of Bias in Large Language Models (LLMs)cited by 19
  6. 2024Automatically Improving LLM-based Verilog Generation using EDA Tool Feedbackcited by 18
  7. 2024Evaluating Fairness in Large Vision-Language Models Across Diverse Demographic Attributes and Promptscited by 15
  8. 2025How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Modelscited by 4
  9. 2026MHSafeEval: Role-Aware Interaction-Level Evaluation of Mental Health Safety in Large Language Modelscited by 3
  10. 2024Passage-specific Prompt Tuning for Passage Reranking in Question Answering with Large Language Modelscited by 2
  11. 2025ReGeS: Reciprocal Retrieval-Generation Synergy for Conversational Recommender Systemscited by 2
  12. 2025Small Changes, Large Consequences: Analyzing the Allocational Fairness of LLMs in Hiring Contextscited by 1
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  2. 2026Not All RAGs Are Created Equal: A Component-Wise Empirical Study for Software Engineering Taskscited by 1
  3. 2025Quantifying Misattribution Unfairness in Authorship Attributioncited by 0
  4. 2025Chitchat with AI: Understand the supply chain carbon disclosure of companies worldwide through Large Language Modelcited by 0
  5. 2025Gender Bias in Emotion Recognition by Large Language Modelscited by 0