413 citations · 3.1k across the 143 of their papers we have counts for
144 papers
Overview of the TREC 2021 deep learning track
Nick Craswell, Bhaskar Mitra, Emine Yilmaz +2
This is the third year of the TREC Deep Learning track. As in previous years, we leverage the MS MARCO datasets that made hundreds of thousands of human annotated training labels a…
Zero-Shot Cross-Lingual Reranking with Large Language Models for Low-Resource Languages
Mofetoluwa Adeyemi, Akintunde Oladipo, Ronak Pradeep +1
Large language models (LLMs) have shown impressive zero-shot capabilities in various document reranking tasks. Despite their successful implementations, there is still a gap in exi…
Scaling Down, LiTting Up: Efficient Zero-Shot Listwise Reranking with Seq2seq Encoder-Decoder Models
Manveer Singh Tamber, Ronak Pradeep, Jimmy Lin
Recent work in zero-shot listwise reranking using LLMs has achieved state-of-the-art results. However, these methods are not without drawbacks. The proposed methods rely on large L…
Rank-without-GPT: Building GPT-Independent Listwise Rerankers on Open-Source Large Language Models
Xinyu Zhang, Sebastian Hofstätter, Patrick Lewis +2
Listwise rerankers based on large language models (LLM) are the zero-shot state-of-the-art. However, current works in this direction all depend on the GPT models, making it a singl…
RankZephyr: Effective and Robust Zero-Shot Listwise Reranking is a Breeze!
Ronak Pradeep, Sahel Sharifymoghaddam, Jimmy Lin
In information retrieval, proprietary large language models (LLMs) such as GPT-4 and open-source counterparts such as LLaMA and Vicuna have played a vital role in reranking. Howeve…
"Knowing When You Don't Know": A Multilingual Relevance Assessment Dataset for Robust Retrieval-Augmented Generation
Nandan Thakur, Luiz Bonifacio, Xinyu Zhang +8
Retrieval-Augmented Generation (RAG) grounds Large Language Model (LLM) output by leveraging external knowledge sources to reduce factual hallucinations. However, prior work lacks…