
Large Language Models (LLMs) exhibit biases in their ranking outcomes, underrepresenting historically marginalized groups. This study empirically evaluates the fairness of popular LLMs, including GPT-3.5, GPT-4, Llama2-13b, and Mistral-7b, as text rankers using listwise and pairwise evaluation methods.


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evaluating-the-fairness-of-large-language-models-as-rankers-an-empirical-study


Evaluating the Fairness of Large Language Models as Rankers: An Empirical Study


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Large language models struggle to effectively rank a small set of items based on multiple, potentially conflicting conditions. A novel decomposed reasoning approach, EXSIR, significantly improves LLM performance on this task.


enhancing-multi-conditional-ranking-with-large-language-models-through-decomposed-reasoning


Enhancing Multi-Conditional Ranking with Large Language Models through Decomposed Reasoning
