4 citations · 11 across the 11 of their papers we have counts for
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cs.IR2024
RaCT: Ranking-aware Chain-of-Thought Optimization for LLMs
Haowei Liu, Xuyang Wu, Guohao Sun +2
In information retrieval, large language models (LLMs) have demonstrated remarkable potential in text reranking tasks by leveraging their sophisticated natural language understandi…
cs.IR2024★ 3 cited
Meta Learning to Rank for Sparsely Supervised Queries
Xuyang Wu, Ajit Puthenputhussery, Hongwei Shang +2
Supervisory signals are a critical resource for training learning to rank models. In many real-world search and retrieval scenarios, these signals may not be readily available or c…
cs.IR2024
Do Large Language Models Rank Fairly? An Empirical Study on the Fairness of LLMs as Rankers
Yuan Wang, Xuyang Wu, Hsin-Tai Wu +2
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 Lla…