43 citations · 118 across the 27 of their papers we have counts for
27 papers
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…
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…