44 citations · 166 across the 21 of their papers we have counts for
22 papers
From Topology to Retrieval: Decoding Embedding Spaces with Unified Signatures
Florian Rottach, William Rudman, Bastian Rieck +2
Studying the shape of text embedding spaces enhances our understanding of model behavior and how geometry affects downstream task performance. While a range of topological and geom…
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…
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…
Variations in Relevance Judgments and the Shelf Life of Test Collections
Andrew Parry, Maik Fröbe, Harrisen Scells +5
The fundamental property of Cranfield-style evaluations, that system rankings are stable even when assessors disagree on individual relevance decisions, was validated on traditiona…
Ranking Generated Answers: On the Agreement of Retrieval Models with Humans on Consumer Health Questions
Sebastian Heineking, Jonas Probst, Daniel Steinbach +2
Evaluating the output of generative large language models (LLMs) is challenging and difficult to scale. Many evaluations of LLMs focus on tasks such as single-choice question-answe…
Learning Effective Representations for Retrieval Using Self-Distillation with Adaptive Relevance Margins
Lukas Gienapp, Niklas Deckers, Martin Potthast +1
Representation-based retrieval models, so-called bi-encoders, estimate the relevance of a document to a query by calculating the similarity of their respective embeddings. Current…
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…
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…
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…
Evaluating Generative Ad Hoc Information Retrieval
Lukas Gienapp, Harrisen Scells, Niklas Deckers +9
Recent advances in large language models have enabled the development of viable generative retrieval systems. Instead of a traditional document ranking, generative retrieval system…
Generating Natural Language Queries for More Effective Systematic Review Screening Prioritisation
Shuai Wang, Harrisen Scells, Martin Potthast +2
Screening prioritisation in medical systematic reviews aims to rank the set of documents retrieved by complex Boolean queries. Prioritising the most important documents ensures tha…
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…
The Archive Query Log: Mining Millions of Search Result Pages of Hundreds of Search Engines from 25 Years of Web Archives
Jan Heinrich Reimer, Sebastian Schmidt, Maik Fröbe +5
The Archive Query Log (AQL) is a previously unused, comprehensive query log collected at the Internet Archive over the last 25 years. Its first version includes 356 million queries…
Can ChatGPT Write a Good Boolean Query for Systematic Review Literature Search?
Shuai Wang, Harrisen Scells, Bevan Koopman +1
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-b…
MeSH Suggester: A Library and System for MeSH Term Suggestion for Systematic Review Boolean Query Construction
Shuai Wang, Hang Li, Guido Zuccon
Boolean query construction is often critical for medical systematic review literature search. To create an effective Boolean query, systematic review researchers typically spend we…
Neural Rankers for Effective Screening Prioritisation in Medical Systematic Review Literature Search
Shuai Wang, Harrisen Scells, Bevan Koopman +1
Medical systematic reviews typically require assessing all the documents retrieved by a search. The reason is two-fold: the task aims for ``total recall''; and documents retrieved…
Guiding Neural Entity Alignment with Compatibility
Bing Liu, Harrisen Scells, Wen Hua +3
Entity Alignment (EA) aims to find equivalent entities between two Knowledge Graphs (KGs). While numerous neural EA models have been devised, they are mainly learned using labelled…
Automated MeSH Term Suggestion for Effective Query Formulation in Systematic Reviews Literature Search
Shuai Wang, Harrisen Scells, Bevan Koopman +1
High-quality medical systematic reviews require comprehensive literature searches to ensure the recommendations and outcomes are sufficiently reliable. Indeed, searching for releva…
From Little Things Big Things Grow: A Collection with Seed Studies for Medical Systematic Review Literature Search
Shuai Wang, Harrisen Scells, Justin Clark +2
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 e…
Seed-driven Document Ranking for Systematic Reviews: A Reproducibility Study
Shuai Wang, Harrisen Scells, Ahmed Mourad +1
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 stud…
MeSH Term Suggestion for Systematic Review Literature Search
Shuai Wang, Hang Li, Harrisen Scells +2
High-quality medical systematic reviews require comprehensive literature searches to ensure the recommendations and outcomes are sufficiently reliable. Indeed, searching for releva…
ActiveEA: Active Learning for Neural Entity Alignment
Bing Liu, Harrisen Scells, Guido Zuccon +2
Entity Alignment (EA) aims to match equivalent entities across different Knowledge Graphs (KGs) and is an essential step of KG fusion. Current mainstream methods -- neural EA model…