18 citations · 64 across the 12 of their papers we have counts for
13 papers
A Reproducibility and Generalizability Study of Large Language Models for Query Generation
Moritz Staudinger, Wojciech Kusa, Florina Piroi +2
Systematic literature reviews (SLRs) are a cornerstone of academic research, yet they are often labour-intensive and time-consuming due to the detailed literature curation process.…
AustroTox: A Dataset for Target-Based Austrian German Offensive Language Detection
Pia Pachinger, Janis Goldzycher, Anna Maria Planitzer +3
Model interpretability in toxicity detection greatly profits from token-level annotations. However, currently such annotations are only available in English. We introduce a dataset…
Aurora-M: Open Source Continual Pre-training for Multilingual Language and Code
Taishi Nakamura, Mayank Mishra, Simone Tedeschi +42
Pretrained language models are an integral part of AI applications, but their high computational cost for training limits accessibility. Initiatives such as Bloom and StarCoder aim…
CSMeD: Bridging the Dataset Gap in Automated Citation Screening for Systematic Literature Reviews
Wojciech Kusa, Oscar E. Mendoza, Matthias Samwald +2
Systematic literature reviews (SLRs) play an essential role in summarising, synthesising and validating scientific evidence. In recent years, there has been a growing interest in u…
CRUISE-Screening: Living Literature Reviews Toolbox
Wojciech Kusa, Petr Knoth, Allan Hanbury
Keeping up with research and finding related work is still a time-consuming task for academics. Researchers sift through thousands of studies to identify a few relevant ones. Autom…
Effective Matching of Patients to Clinical Trials using Entity Extraction and Neural Re-ranking
Wojciech Kusa, Óscar E. Mendoza, Petr Knoth +2
Clinical trials (CTs) often fail due to inadequate patient recruitment. This paper tackles the challenges of CT retrieval by presenting an approach that addresses the patient-to-tr…
Outcome-based Evaluation of Systematic Review Automation
Wojciech Kusa, Guido Zuccon, Petr Knoth +1
Current methods of evaluating search strategies and automated citation screening for systematic literature reviews typically rely on counting the number of relevant and not relevan…
Statute-enhanced lexical retrieval of court cases for COLIEE 2022
Tobias Fink, Gabor Recski, Wojciech Kusa +1
We discuss our experiments for COLIEE Task 1, a court case retrieval competition using cases from the Federal Court of Canada. During experiments on the training data we observe th…
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
BigScience Workshop, :, Teven Le Scao +391
Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to wi…
BigBIO: A Framework for Data-Centric Biomedical Natural Language Processing
Jason Alan Fries, Leon Weber, Natasha Seelam +40
Training and evaluating language models increasingly requires the construction of meta-datasets --diverse collections of curated data with clear provenance. Natural language prompt…
ORCAS-I: Queries Annotated with Intent using Weak Supervision
Daria Alexander, Wojciech Kusa, Arjen P. de Vries
User intent classification is an important task in information retrieval. In this work, we introduce a revised taxonomy of user intent. We take the widely used differentiation betw…
Automation of Citation Screening for Systematic Literature Reviews using Neural Networks: A Replicability Study
Wojciech Kusa, Allan Hanbury, Petr Knoth
In the process of Systematic Literature Review, citation screening is estimated to be one of the most time-consuming steps. Multiple approaches to automate it using various machine…
External Evaluation of Event Extraction Classifiers for Automatic Pathway Curation: An extended study of the mTOR pathway
Wojciech Kusa, Michael Spranger
This paper evaluates the impact of various event extraction systems on automatic pathway curation using the popular mTOR pathway. We quantify the impact of training data sets as we…