44 citations · 166 across the 21 of their papers we have counts for
18 papers · 1 filter
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…