Team IELAB at TREC Clinical Trial Track 2023: Enhancing Clinical Trial Retrieval with Neural Rankers and Large Language Models

Multi-stage document ranking with bert

Original

Rodrigo Nogueira, Wei Yang, Kyunghyun Cho, and Jimmy Lin. 2019 · 1910

Earlier work this paper cites.

Document ranking with a pretrained sequence-to-sequence model

Rodrigo Nogueira, Zhiying Jiang, Ronak Pradeep, and Jimmy Lin. 2020 · 2020

Earlier work this paper cites.

Splade v2: Sparse lexical and expansion model for information retrieval

Original

Thibault Formal, Carlos Lassance, Benjamin Piwowarski, and Stéphane Clinchant. 2021 · 2021

Earlier work this paper cites.

Rethink training of bert rerankers in multi-stage retrieval pipeline

Luyu Gao, Zhuyun Dai, and Jamie Callan. 2021 · 2021

Earlier work this paper cites.

Bert-based dense retrievers require interpolation with bm25 for effective passage retrieval

Shuai Wang, Shengyao Zhuang, and Guido Zuccon. 2021 · 2021

Cited alongside, same era.

Approximate nearest neighbor negative contrastive learning for dense text retrieval

Lee Xiong, Chenyan Xiong, Ye Li, Kwok-Fung Tang, Jialin Liu, Paul N. Bennett, Junaid Ahmed, and Arnold Overwijk. 2021 · 2021

Cited alongside, same era.

Pretrained transformers for text ranking: BERT and beyond

Andrew Yates, Rodrigo Nogueira, and Jimmy Lin. 2021 · 2021

Cited alongside, same era.

Unsupervised corpus aware language model pre-training for dense passage retrieval

Luyu Gao and Jamie Callan. 2022 · 2022

Cited alongside, same era.

Simlm: Pre-training with representation bottleneck for dense passage retrieval

Original

Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, and Furu Wei. 2022 · 2022

Later among the works it cites.

Large language models can accurately predict searcher preferences

Original

Paul Thomas, Seth Spielman, Nick Craswell, and Bhaskar Mitra. 2023 · 2023

Later among the works it cites.

Fine-tuning large neural language models for biomedical natural language processing

Robert Tinn, Hao Cheng, Yu Gu, Naoto Usuyama, Xiaodong Liu, Tristan Naumann, Jianfeng Gao, and Hoifung Poon. 2023 · 2023

Later among the works it cites.

Typos-aware bottlenecked pre-training for robust dense retrieval

Original

Shengyao Zhuang, Linjun Shou, Jian Pei, Ming Gong, Houxing Ren, Guido Zuccon, and Daxin Jiang. 2023 · 2023

Later among the works it cites.