Is ChatGPT good at search? investigating large language models as re-ranking agents

W Sun, L Yan, X Ma, S Wang, P Ren, Z Chen… - arXiv preprint arXiv …, 2023 - arxiv.org
Large Language Models (LLMs) have demonstrated remarkable zero-shot generalization
across various language-related tasks, including search engines. However, existing work …

Dense text retrieval based on pretrained language models: A survey

WX Zhao, J Liu, R Ren, JR Wen - ACM Transactions on Information …, 2024 - dl.acm.org
Text retrieval is a long-standing research topic on information seeking, where a system is
required to return relevant information resources to user's queries in natural language. From …

T2ranking: A large-scale chinese benchmark for passage ranking

X Xie, Q Dong, B Wang, F Lv, T Yao, W Gan… - Proceedings of the 46th …, 2023 - dl.acm.org
Passage ranking involves two stages: passage retrieval and passage re-ranking, which are
important and challenging topics for both academics and industries in the area of …

One-shot labeling for automatic relevance estimation

S MacAvaney, L Soldaini - Proceedings of the 46th International ACM …, 2023 - dl.acm.org
Dealing with unjudged documents (" holes") in relevance assessments is a perennial
problem when evaluating search systems with offline experiments. Holes can reduce the …

Hindsight: Posterior-guided training of retrievers for improved open-ended generation

A Paranjape, O Khattab, C Potts, M Zaharia… - arXiv preprint arXiv …, 2021 - arxiv.org
Many text generation systems benefit from using a retriever to retrieve passages from a
textual knowledge corpus (eg, Wikipedia) which are then provided as additional context to …

Leveraging llms for unsupervised dense retriever ranking

E Khramtsova, S Zhuang, M Baktashmotlagh… - Proceedings of the 47th …, 2024 - dl.acm.org
In this paper we present Large Language Model Assisted Retrieval Model Ranking
(LARMOR), an effective unsupervised approach that leverages LLMs for selecting which …

Introducing neural bag of whole-words with colberter: Contextualized late interactions using enhanced reduction

S Hofstätter, O Khattab, S Althammer… - Proceedings of the 31st …, 2022 - dl.acm.org
Recent progress in neural information retrieval has demonstrated large gains in quality,
while often sacrificing efficiency and interpretability compared to classical approaches. We …

Systematic evaluation of neural retrieval models on the touché 2020 argument retrieval subset of BEIR

N Thakur, L Bonifacio, M Fröbe, A Bondarenko… - Proceedings of the 47th …, 2024 - dl.acm.org
The zero-shot effectiveness of neural retrieval models is often evaluated on the BEIR
benchmark---a combination of different IR evaluation datasets. Interestingly, previous …

Rethinking the role of token retrieval in multi-vector retrieval

J Lee, Z Dai, SMK Duddu, T Lei… - Advances in …, 2024 - proceedings.neurips.cc
Multi-vector retrieval models such as ColBERT [Khattab et al., 2020] allow token-level
interactions between queries and documents, and hence achieve state of the art on many …

Noisy perturbations for estimating query difficulty in dense retrievers

N Arabzadeh, R Hamidi Rad, M Khodabakhsh… - Proceedings of the …, 2023 - dl.acm.org
Estimating query difficulty, also known as Query Performance Prediction (QPP), is
concerned with assessing the retrieval quality of a ranking method for an input query. Most …