57 citations · 263 across the 45 of their papers we have counts for
7 papers · 2 filters
Open-source Large Language Models are Strong Zero-shot Query Likelihood Models for Document Ranking
Shengyao Zhuang, Bing Liu, Bevan Koopman +1
In the field of information retrieval, Query Likelihood Models (QLMs) rank documents based on the probability of generating the query given the content of a document. Recently, adv…
A Setwise Approach for Effective and Highly Efficient Zero-shot Ranking with Large Language Models
Shengyao Zhuang, Honglei Zhuang, Bevan Koopman +1
We propose a novel zero-shot document ranking approach based on Large Language Models (LLMs): the Setwise prompting approach. Our approach complements existing prompting approaches…
Selecting which Dense Retriever to use for Zero-Shot Search
Ekaterina Khramtsova, Shengyao Zhuang, Mahsa Baktashmotlagh +2
We propose the new problem of choosing which dense retrieval model to use when searching on a new collection for which no labels are available, i.e. in a zero-shot setting. Many de…
Exploring the Representation Power of SPLADE Models
Joel Mackenzie, Shengyao Zhuang, Guido Zuccon
The SPLADE (SParse Lexical AnD Expansion) model is a highly effective approach to learned sparse retrieval, where documents are represented by term impact scores derived from large…
Beyond CO2 Emissions: The Overlooked Impact of Water Consumption of Information Retrieval Models
Guido Zuccon, Harrisen Scells, Shengyao Zhuang
As in other fields of artificial intelligence, the information retrieval community has grown interested in investigating the power consumption associated with neural models, partic…
Augmenting Passage Representations with Query Generation for Enhanced Cross-Lingual Dense Retrieval
Shengyao Zhuang, Linjun Shou, Guido Zuccon
Effective cross-lingual dense retrieval methods that rely on multilingual pre-trained language models (PLMs) need to be trained to encompass both the relevance matching task and th…
Typos-aware Bottlenecked Pre-Training for Robust Dense Retrieval
Shengyao Zhuang, Linjun Shou, Jian Pei +4
Current dense retrievers (DRs) are limited in their ability to effectively process misspelled queries, which constitute a significant portion of query traffic in commercial search…