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Student Travel Scholarship

2018 student travel scholarship for exchange to University of Glasgow

DIGIX Global AI challenge (Champion)

Champion in the 2021 HUAWEI DIGIX GLOBAL AI CHALLENGE Track 4

UQ Earmarked PhD Scholarship

UQ Earmarked scholarship for tuition fee and living stipend

Best Student Paper Award

Best student paper award on ADCS 2021 for paper MeSH Term Suggestion for Systematic Review Literature Search

Dean’s commendation for academic excellence

For academic achievement in sem 1, 2021, The University of Queensland

SIGIR Travel Grant

$1000 Travel Grant for SIGIR 2022, Madrid, Spain

SIGIR Travel Grant

$1125 Travel Grant for SIGIR 2023, Taipei

BERT-based Dense Retrievers Require Interpolation with BM25 for Effective Passage Retrieval

Shuai Wang and Shengyao Zhuang and Guido Zuccon.

Abstract The integration of deep, pre-trained language models, such as BERT, into retrieval and ranking pipelines has shown to provide large effectiveness gains over traditional bag-of-words models in the passage retr...

Paper

IELAB at TREC Deep Learning Track 2021

Shengyao Zhuang and Hang Li and Shuai Wang and Guido Zuccon.

Abstract

Paper

MeSH Term Suggestion for Systematic Review Literature Search

Shuai Wang and Hang Li and Harry Scells and Daniel Locke and Guido Zuccon.

Abstract High-quality medical systematic reviews require comprehensive literature searches to ensure the recommendations and outcomes are sufficiently reliable. Indeed, searching for relevant medical literature is a k...

Paper

SDR for Systematic Reviews: A Reproducibility Study

Shuai Wang and Harry Scells and Ahmed Mourad and Guido Zuccon.

Abstract Screening or assessing studies is critical to the quality and outcomes of a systematic review. Typically, a Boolean query retrieves the set of studies to screen. As the set of studies retrieved is unordered, ...

Paper

From Little Things Big Things Grow: A Collection with Seed Studies for Medical Systematic Review Literature Search

Shuai Wang and Harry Scells and Justin Clark and Guido Zuccon and Bevan Koopman.

Abstract Medical systematic review query formulation is a highly complex task done by trained information specialists. Complexity comes from the reliance on lengthy Boolean queries, which express a detailed research q...

Paper

To Interpolate or not to Interpolate: PRF, Dense and Sparse Retrievers

Hang Li* and Shuai Wang* and Shengyao Zhuang and Ahmed Mourad and xueguang-ma and jimmy-lin and Guido Zuccon.

Abstract Current pre-trained language model approaches to information retrieval can be broadly divided into two categories: sparse retrievers (to which belong also non-neural approaches such as bag-of-words methods, e...

Paper

Automated MeSH Term Suggestion for Effective Query Formulation in Systematic Reviews Literature Search

Shuai Wang and Harry Scells and Bevan Koopman and Guido Zuccon.

Abstract Medical systematic review query formulation is a highly complex task done by trained information specialists. Complexity comes from the reliance on lengthy Boolean queries, which express a detailed research q...

Paper

Neural Rankers for Effective Screening Prioritization in Medical Systematic Review Literature Search

Shuai Wang and Harry Scells and Bevan Koopman and Guido Zuccon.

Abstract Medical systematic reviews typically require that all the documents retrieved by a search are assessed. The reason is two-fold: the task aims for “total recall”; and documents retrieved using Boolean search a...

Paper

MeSH Suggester: A Library and System for MeSH Term Suggestion for Systematic Review Boolean Query Construction

Shuai Wang and Hang Li and Guido Zuccon.

Abstract Boolean query construction is often critical for medical systematic review literature search. To create an effective Boolean query, sys- tematic review researchers typically spend weeks coming up with effecti...

Paper Demo

Balanced Topic Aware Sampling for Effective Dense Retriever: A Reproducibility Study

Shuai Wang, and Guido Zuccon.

Abstract Knowledge distillation plays a key role in boosting the effectiveness of rankers based on pre-trained language models (PLMs); this is achieved using an effective but inefficient large model to teach a more ef...

Paper

Can ChatGPT Write a Good Boolean Query for Systematic Review Literature Search?

Shuai Wang, Harrisen Scells, Bevan Koopman and Guido Zuccon.

Abstract Systematic reviews are comprehensive reviews of the literature for a highly focused research question. These reviews are often treated as the highest form of evidence in evidence-based medicine, and are the k...

Paper

Generating Natural Language Queries for More Effective Systematic Review Screening Prioritisation

Shuai Wang, Harrisen Scells, Martin Potthast, Bevan Koopman and Guido Zuccon.

Abstract Screening prioritisation in medical systematic reviews aims to rank the set of documents retrieved by complex Boolean queries. The goal is to prioritise the most important documents so that subsequent review ...

Paper

Zero-shot Generative Large Language Models for Systematic Review Screening Automation

Shuai Wang, Harrisen Scells, Shengyao Zhuang, Martin Potthast, Bevan Koopman and Guido Zuccon.

Abstract Systematic reviews are crucial for evidence-based medicine as they comprehensively analyse published research findings on specific questions. Conducting such reviews is often resource- and time-intensive, esp...

Paper

ReSLLM: Large Language Models are Strong Resource Selectors for Federated Search

Shuai Wang, Shengyao Zhuang, Bevan Koopman and Guido Zuccon.

Abstract Federated search, which involves integrating results from multiple independent search engines, will become increasingly pivotal in the context of Retrieval-Augmented Generation pipelines empowering LLM-based ...

Paper

FeB4RAG: Evaluating Federated Search in the Context of Retrieval Augmented Generation

Shuai Wang, Ekaterina Khramtsova, Shengyao Zhuang and Guido Zuccon.

Abstract Federated search systems aggregate results from multiple search engines, selecting appropriate sources to enhance result quality and align with user intent. With the increasing uptake of Retrieval-Augmented G...

Paper

Evaluating Generative Ad Hoc Information Retrieval

Lukas Gienapp, Harrisen Scells, Niklas Deckers, Janek Bevendorff, Shuai Wang, Johannes Kiesel, Shahbaz Syed, Maik Fröbe, Guido Zuccon, Benno Stein, Matthias Hagen and Martin Potthast.

Abstract Recent advances in large language models have enabled the development of viable generative information retrieval systems. A generative retrieval system returns a grounded generated text in response to an info...

Paper

Large Language Models for Stemming: Promises, Pitfalls and Failures

Shuai Wang, Shengyao Zhuang and Guido Zuccon.

Abstract Text stemming is a natural language processing technique that is used to reduce words to their base form, also known as the root form. The use of stemming in IR has been shown to often improve the effectivene...

Paper

Zero-shot Generative Large Language Models for Systematic Review Screening Automation

Shuoqi Sun, Shengyao Zhuang, Shuai Wang and Guido Zuccon.

Abstract We provide a systematic understanding of the impact of specific components and wordings used in prompts on the effectiveness of rankers based on zero-shot Large Language Models (LLMs). Several zero-shot ranki...

Paper

BERGEN: A Benchmarking Library for Retrieval-Augmented Generation

David Rau, Hervé Déjean, Nadezhda Chirkova, Thibault Formal, Shuai Wang, Vassilina Nikoulina and Stéphane Clinchant.

Abstract Retrieval-Augmented Generation allows to enhance Large Language Models with external knowledge. In response to the recent popularity of generative LLMs, many RAG approaches have been proposed, which involve a...

Paper

Context Embeddings for Efficient Answer Generation in RAG

David Rau*, Shuai Wang*, Hervé Déjean and Stéphane Clinchant.

Abstract Retrieval-Augmented Generation (RAG) allows overcoming the limited knowledge of LLMs by extending the input with external information. As a consequence, the contextual inputs to the model become much longer w...

Paper

Starbucks: Improved Training for 2D Matryoshka Embeddings

Shengyao Zhuang*, Shuai Wang*, Bevan Koopman and Guido Zuccon.

Abstract Effective approaches that can scale embedding model depth (i.e. layers) and embedding size allow for the creation of models that are highly scalable across different computational resources and task requireme...

Paper

Corpus Subsampling: Estimating the Effectiveness of Neural Retrieval Models on Large Corpora

Maik Fröbe, Andrew Parry, Harrisen Scells, Shuai Wang, Shengyao Zhuang, Guido Zuccon, Martin Potthast and Matthias Hagen.

Abstract Due to their low efficiency, neural retrieval models are usually evaluated on small corpora (e.g. MS MARCO or BEIR subsets) or in re-ranking scenarios using a more efficient first-stage retriever. To estimate...

Paper

2D Matryoshka Training for Information Retrieval

Shuai Wang, Shengyao Zhuang, Bevan Koopman and Guido Zuccon.

Abstract 2D Matryoshka Training is an advanced embedding representation training approach designed to train an encoder model simultaneously across various layer-dimension setups. This method has demonstrated higher ef...

Paper

Reassessing Large Language Model Boolean Query Generation for Systematic Reviews

Shuai Wang, Harrisen Scells, Bevan Koopman and Guido Zuccon.

Abstract Systematic reviews are comprehensive literature reviews that address highly focused research questions and represent the highest form of evidence in medicine. A critical step in this process is the developmen...

Paper

Pre-training vs. Fine-tuning: A Reproducibility Study on Dense Retrieval Knowledge Acquisition

Zheng Yao, Shuai Wang and Guido Zuccon.

Abstract Dense retrievers utilize pre-trained backbone language models (e.g., BERT, LLaMA) that are fine-tuned via contrastive learning to perform the task of encoding text into sense representations that can be then ...

Paper

Evalugator🐊—Rapid, Agile Development and Evaluation of Retrieval Augmented Generation Systems Without Labels

Bevan Koopman, Hang Li, Shuai Wang and Guido Zuccon.

Abstract Evaluating complex Retrieval Augmented Generation (RAG) systems in real-world settings is challenging. There is often a lack of finegrained labelled data and the absence of comprehensive evaluation tools tha...

Paper

AutoBool: Reinforcement-Learned LLM for Effective Automatic Systematic Reviews Boolean Query Generation

Shuai Wang, Harrisen Scells, Bevan Koopman and Guido Zuccon.

Abstract We present AutoBool, a reinforcement learning (RL) framework that trains large language models (LLMs) to generate effective Boolean queries for medical systematic reviews. Boolean queries are the primary mech...

Paper Demo

Beyond Chunk-Then-Embed: A Comprehensive Taxonomy and Evaluation of Document Segmentation Strategies for Information Retrieval

Yongjie Zhou*, Shuai Wang*, Bevan Koopman and Guido Zuccon.

Abstract Document chunking is a critical preprocessing step in dense retrieval systems, yet the design space of chunking strategies remains poorly understood. Recent research has proposed several concurrent approaches...

Paper

Rank-R1: Enhancing Reasoning in LLM-based Document Rerankers via Reinforcement Learning

Shengyao Zhuang, Xueguang Ma, Zheng Yao, Shuai Wang, Bevan Koopman, Jimmy Lin and Guido Zuccon.

Abstract This paper introduces Rank-R1, a novel LLM-based reranker that performs reasoning over both the user query and candidate documents before performing the ranking task. Existing document reranking methods based...

Paper

The Vulnerability of LLM Rankers to Prompt Injection Attacks

Yu Yin, Shuai Wang, Bevan Koopman and Guido Zuccon.

Abstract Large Language Models (LLMs) have emerged as powerful re-rankers. Recent research has shown that simple prompt injections embedded within a candidate document (jailbreak prompt attacks) can significantly alte...

Paper

Dr. Shuai Wang (Dylan)

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