57 citations · 422 across the 74 of their papers we have counts for
75 papers
Can It Reach the Generator? Investigating the Survival of Prompt-Injection Attacks in Realistic RAG Settings
Yu Yin, Shuai Wang, Bevan Koopman +1
Recent generative engine optimisation (GEO) research has shown that prompt-injection attacks can push a target product to the top of an LLM's recommendation list, with the stronges…
Beyond Chunk-Then-Embed: A Comprehensive Taxonomy and Evaluation of Document Chunking Strategies for Information Retrieval
Yongjie Zhou, Shuai Wang, Bevan Koopman +1
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 s…
The Vulnerability of LLM Rankers to Prompt Injection Attacks
Yu Yin, Shuai Wang, Bevan Koopman +1
Large Language Models (LLMs) have emerged as powerful re-rankers. Recent research has however showed that simple prompt injections embedded within a candidate document (i.e., jailb…
AutoBool: An Reinforcement-Learning trained LLM for Effective Automated Boolean Query Generation for Systematic Reviews
Shuai Wang, Harrisen Scells, Bevan Koopman +1
We present AutoBool, a reinforcement learning (RL) framework that trains large language models (LLMs) to generate effective Boolean queries for medical systematic reviews. Boolean…
Humans are more gullible than LLMs in believing common psychological myths
Bevan Koopman, Guido Zuccon
Despite widespread debunking, many psychological myths remain deeply entrenched. This paper investigates whether Large Language Models (LLMs) mimic human behaviour of myth belief a…
Reassessing Large Language Model Boolean Query Generation for Systematic Reviews
Shuai Wang, Harrisen Scells, Bevan Koopman +1
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 th…
Unlearning for Federated Online Learning to Rank: A Reproducibility Study
Yiling Tao, Shuyi Wang, Jiaxi Yang +1
This paper reports on findings from a comparative study on the effectiveness and efficiency of federated unlearning strategies within Federated Online Learning to Rank (FOLTR), wit…
Pre-training vs. Fine-tuning: A Reproducibility Study on Dense Retrieval Knowledge Acquisition
Zheng Yao, Shuai Wang, Guido Zuccon
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 repre…
AiReview: An Open Platform for Accelerating Systematic Reviews with LLMs
Xinyu Mao, Teerapong Leelanupab, Martin Potthast +2
Systematic reviews are fundamental to evidence-based medicine. Creating one is time-consuming and labour-intensive, mainly due to the need to screen, or assess, many studies for in…
LLM-VPRF: Large Language Model Based Vector Pseudo Relevance Feedback
Hang Li, Shengyao Zhuang, Bevan Koopman +1
Vector Pseudo Relevance Feedback (VPRF) has shown promising results in improving BERT-based dense retrieval systems through iterative refinement of query representations. This pape…
Rank-R1: Enhancing Reasoning in LLM-based Document Rerankers via Reinforcement Learning
Shengyao Zhuang, Xueguang Ma, Bevan Koopman +2
In this paper, we introduce Rank-R1, a novel LLM-based reranker that performs reasoning over both the user query and candidate documents before performing the ranking task. Existin…
Leveraging Semantic Type Dependencies for Clinical Named Entity Recognition
Linh Le, Guido Zuccon, Gianluca Demartini +2
Previous work on clinical relation extraction from free-text sentences leveraged information about semantic types from clinical knowledge bases as a part of entity representations.…
Pseudo Relevance Feedback is Enough to Close the Gap Between Small and Large Dense Retrieval Models
Hang Li, Xiao Wang, Bevan Koopman +1
Scaling dense retrievers to larger large language model (LLM) backbones has been a dominant strategy for improving their retrieval effectiveness. However, this has substantial cost…
DenseReviewer: A Screening Prioritisation Tool for Systematic Review based on Dense Retrieval
Xinyu Mao, Teerapong Leelanupab, Harrisen Scells +1
Screening is a time-consuming and labour-intensive yet required task for medical systematic reviews, as tens of thousands of studies often need to be screened. Prioritising relevan…
Document Screenshot Retrievers are Vulnerable to Pixel Poisoning Attacks
Shengyao Zhuang, Ekaterina Khramtsova, Xueguang Ma +3
Recent advancements in dense retrieval have introduced vision-language model (VLM)-based retrievers, such as DSE and ColPali, which leverage document screenshots embedded as vector…
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…