Xuanhui Wang


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PaRaDe: Passage Ranking using Demonstrations with LLMs
Andrew Drozdov | Honglei Zhuang | Zhuyun Dai | Zhen Qin | Razieh Rahimi | Xuanhui Wang | Dana Alon | Mohit Iyyer | Andrew McCallum | Donald Metzler | Kai Hui
Findings of the Association for Computational Linguistics: EMNLP 2023

Recent studies show that large language models (LLMs) can be instructed to effectively perform zero-shot passage re-ranking, in which the results of a first stage retrieval method, such as BM25, are rated and reordered to improve relevance. In this work, we improve LLM-based re-ranking by algorithmically selecting few-shot demonstrations to include in the prompt. Our analysis investigates the conditions where demonstrations are most helpful, and shows that adding even one demonstration is significantly beneficial. We propose a novel demonstration selection strategy based on difficulty rather than the commonly used semantic similarity. Furthermore, we find that demonstrations helpful for ranking are also effective at question generation. We hope our work will spur more principled research into question generation and passage ranking.


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Language Model Information Retrieval with Document Expansion
Tao Tao | Xuanhui Wang | Qiaozhu Mei | ChengXiang Zhai
Proceedings of the Human Language Technology Conference of the NAACL, Main Conference