Do Large Language Models Rank Fairly? An Empirical Study on the Fairness of LLMs as Rankers
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.
Code (0)
등록된 구현이 없습니다.
Tasks
FairnessInformation RetrievalNatural Language UnderstandingRetrievalMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Subverting Fair Image Search with Generative Adversarial Perturbations
In this work we explore the intersection fairness and robustness in the context of ranking: when a ranking model has been calibrated to achieve some definition of fairness, is it possible for an external adversary to mak…
FairnessImage RetrievalRe-RankingLanguage Model Council: Democratically Benchmarking Foundation Models on Highly Subjective Tasks
As Large Language Models (LLMs) continue to evolve, the search for efficient and meaningful evaluation methods is ongoing. Many recent evaluations use LLMs as judges to score outputs from other LLMs, often relying on a s…
BenchmarkingChatbotEmotional IntelligenceLanguage Modeling+2A Distributed Frank-Wolfe Algorithm for Communication-Efficient Sparse Learning
Learning sparse combinations is a frequent theme in machine learning. In this paper, we study its associated optimization problem in the distributed setting where the elements to be combined are not centrally located but…
Sparse LearningAn Empirical Analysis of GPT-4V's Performance on Fashion Aesthetic Evaluation
Fashion aesthetic evaluation is the task of estimating how well the outfits worn by individuals in images suit them. In this work, we examine the zero-shot performance of GPT-4V on this task for the first time. We show t…
Don't Judge Code by Its Cover: Exploring Biases in LLM Judges for Code Evaluation
With the growing use of large language models(LLMs) as evaluators, their application has expanded to code evaluation tasks, where they assess the correctness of generated code without relying on reference implementations…