57 citations · 422 across the 74 of their papers we have counts for
20 papers · 1 filter
VISA: Retrieval Augmented Generation with Visual Source Attribution
Xueguang Ma, Shengyao Zhuang, Bevan Koopman +3
Generation with source attribution is important for enhancing the verifiability of retrieval-augmented generation (RAG) systems. However, existing approaches in RAG primarily link…
2D Matryoshka Training for Information Retrieval
Shuai Wang, Shengyao Zhuang, Bevan Koopman +1
2D Matryoshka Training is an advanced embedding representation training approach designed to train an encoder model simultaneously across various layer-dimension setups. This metho…
Does Vec2Text Pose a New Corpus Poisoning Threat?
Shengyao Zhuang, Bevan Koopman, Guido Zuccon
The emergence of Vec2Text -- a method for text embedding inversion -- has raised serious privacy concerns for dense retrieval systems which use text embeddings. This threat comes f…
Starbucks-v2: Improved Training for 2D Matryoshka Embeddings
Shengyao Zhuang, Shuai Wang, Fabio Zheng +2
2D Matryoshka training enables a single embedding model to generate sub-network representations across different layers and embedding dimensions, offering adaptability to diverse c…
Dense Retrieval with Continuous Explicit Feedback for Systematic Review Screening Prioritisation
Xinyu Mao, Shengyao Zhuang, Bevan Koopman +1
The goal of screening prioritisation in systematic reviews is to identify relevant documents with high recall and rank them in early positions for review. This saves reviewing effo…
Embark on DenseQuest: A System for Selecting the Best Dense Retriever for a Custom Collection
Ekaterina Khramtsova, Teerapong Leelanupab, Shengyao Zhuang +2
In this demo we present a web-based application for selecting an effective pre-trained dense retriever to use on a private collection. Our system, DenseQuest, provides unsupervised…
An Investigation of Prompt Variations for Zero-shot LLM-based Rankers
Shuoqi Sun, Shengyao Zhuang, Shuai Wang +1
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 (LL…
Rank-DistiLLM: Closing the Effectiveness Gap Between Cross-Encoders and LLMs for Passage Re-Ranking
Ferdinand Schlatt, Maik Fröbe, Harrisen Scells +6
Cross-encoders distilled from large language models (LLMs) are often more effective re-rankers than cross-encoders fine-tuned on manually labeled data. However, distilled models do…
PromptReps: Prompting Large Language Models to Generate Dense and Sparse Representations for Zero-Shot Document Retrieval
Shengyao Zhuang, Xueguang Ma, Bevan Koopman +2
Utilizing large language models (LLMs) for zero-shot document ranking is done in one of two ways: (1) prompt-based re-ranking methods, which require no further training but are onl…
Set-Encoder: Permutation-Invariant Inter-Passage Attention for Listwise Passage Re-Ranking with Cross-Encoders
Ferdinand Schlatt, Maik Fröbe, Harrisen Scells +6
Existing cross-encoder models can be categorized as pointwise, pairwise, or listwise. Pairwise and listwise models allow passage interactions, which typically makes them more effec…
FeB4RAG: Evaluating Federated Search in the Context of Retrieval Augmented Generation
Shuai Wang, Ekaterina Khramtsova, Shengyao Zhuang +1
Federated search systems aggregate results from multiple search engines, selecting appropriate sources to enhance result quality and align with user intent. With the increasing upt…
Large Language Models for Stemming: Promises, Pitfalls and Failures
Shuai Wang, Shengyao Zhuang, Guido Zuccon
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 o…
Understanding and Mitigating the Threat of Vec2Text to Dense Retrieval Systems
Shengyao Zhuang, Bevan Koopman, Xiaoran Chu +1
The emergence of Vec2Text -- a method for text embedding inversion -- has raised serious privacy concerns for dense retrieval systems which use text embeddings, such as those offer…
Leveraging LLMs for Unsupervised Dense Retriever Ranking
Ekaterina Khramtsova, Shengyao Zhuang, Mahsa Baktashmotlagh +1
In this paper we present Large Language Model Assisted Retrieval Model Ranking (LARMOR), an effective unsupervised approach that leverages LLMs for selecting which dense retriever…
Zero-shot Generative Large Language Models for Systematic Review Screening Automation
Shuai Wang, Harrisen Scells, Shengyao Zhuang +3
Systematic reviews are crucial for evidence-based medicine as they comprehensively analyse published research findings on specific questions. Conducting such reviews is often resou…
ReSLLM: Large Language Models are Strong Resource Selectors for Federated Search
Shuai Wang, Shengyao Zhuang, Bevan Koopman +1
Federated search, which involves integrating results from multiple independent search engines, will become increasingly pivotal in the context of Retrieval-Augmented Generation pip…
How to Forget Clients in Federated Online Learning to Rank?
Shuyi Wang, Bing Liu, Guido Zuccon
Data protection legislation like the European Union's General Data Protection Regulation (GDPR) establishes the \textit{right to be forgotten}: a user (client) can request contribu…
A Reproducibility Study of Goldilocks: Just-Right Tuning of BERT for TAR
Xinyu Mao, Bevan Koopman, Guido Zuccon
Screening documents is a tedious and time-consuming aspect of high-recall retrieval tasks, such as compiling a systematic literature review, where the goal is to identify all relev…
Team IELAB at TREC Clinical Trial Track 2023: Enhancing Clinical Trial Retrieval with Neural Rankers and Large Language Models
Shengyao Zhuang, Bevan Koopman, Guido Zuccon
We describe team ielab from CSIRO and The University of Queensland's approach to the 2023 TREC Clinical Trials Track. Our approach was to use neural rankers but to utilise Large La…
TPRF: A Transformer-based Pseudo-Relevance Feedback Model for Efficient and Effective Retrieval
Hang Li, Chuting Yu, Ahmed Mourad +2
This paper considers Pseudo-Relevance Feedback (PRF) methods for dense retrievers in a resource constrained environment such as that of cheap cloud instances or embedded systems (e…