5 citations · 5 across the 6 of their papers we have counts for
7 papers
Understanding Wacky Weights: A Dissection of SPLADE's Learned Term Importance
Gregory Polyakov, Harrisen Scells, Carsten Eickhoff
Learned sparse retrieval models such as SPLADE combine the effectiveness of neural architectures with the efficiency of inverted indices. As these models assign weights to terms fr…
A Large-Scale, Cross-Disciplinary Corpus of Systematic Reviews
Pierre Achkar, Tim Gollub, Arno Simons +2
Existing benchmarks for systematic reviewing remain limited either in scale or in disciplinary coverage, with some collections comprising only a modest number of topics and others…
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…
Investigating Counterclaims in Causality Extraction from Text
Tim Hagen, Niklas Deckers, Felix Wolter +2
Many causal claims, such as "sugar causes hyperactivity," are disputed or outdated. Yet research on causality extraction from text has almost entirely neglected counterclaims of ca…
Topic-Specific Classifiers are Better Relevance Judges than Prompted LLMs
Lukas Gienapp, Martin Potthast, Andrew Yates +2
The unjudged document problem, where systems that did not contribute to the original judgement pool may retrieve documents without a relevance judgement, is a key obstacle to the r…
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…
The Viability of Crowdsourcing for RAG Evaluation
Lukas Gienapp, Tim Hagen, Maik Fröbe +4
How good are humans at writing and judging responses in retrieval-augmented generation (RAG) scenarios? To answer this question, we investigate the efficacy of crowdsourcing for RA…