16 citations · 50 across the 27 of their papers we have counts for
9 papers · 1 filter
Seemingly Plausible Distractors in Multi-Hop Reasoning: Are Large Language Models Attentive Readers?
Neeladri Bhuiya, Viktor Schlegel, Stefan Winkler
State-of-the-art Large Language Models (LLMs) are accredited with an increasing number of different capabilities, ranging from reading comprehension, over advanced mathematical and…
Learning to Generate and Evaluate Fact-checking Explanations with Transformers
Darius Feher, Abdullah Khered, Hao Zhang +2
In an era increasingly dominated by digital platforms, the spread of misinformation poses a significant challenge, highlighting the need for solutions capable of assessing informat…
Representation Learning of Structured Data for Medical Foundation Models
Vijay Prakash Dwivedi, Viktor Schlegel, Andy T. Liu +6
Large Language Models (LLMs) have demonstrated remarkable performance across various domains, including healthcare. However, their ability to effectively represent structured non-t…
uMedSum: A Unified Framework for Advancing Medical Abstractive Summarization
Aishik Nagar, Yutong Liu, Andy T. Liu +6
Medical abstractive summarization faces the challenge of balancing faithfulness and informativeness. Current methods often sacrifice key information for faithfulness or introduce c…
Investigating a Benchmark for Training-set free Evaluation of Linguistic Capabilities in Machine Reading Comprehension
Viktor Schlegel, Goran Nenadic, Riza Batista-Navarro
Performance of NLP systems is typically evaluated by collecting a large-scale dataset by means of crowd-sourcing to train a data-driven model and evaluate it on a held-out portion…
MEDSAGE: Enhancing Robustness of Medical Dialogue Summarization to ASR Errors with LLM-generated Synthetic Dialogues
Kuluhan Binici, Abhinav Ramesh Kashyap, Viktor Schlegel +6
Automatic Speech Recognition (ASR) systems are pivotal in transcribing speech into text, yet the errors they introduce can significantly degrade the performance of downstream tasks…
LLMs are not Zero-Shot Reasoners for Biomedical Information Extraction
Aishik Nagar, Viktor Schlegel, Thanh-Tung Nguyen +4
Large Language Models (LLMs) are increasingly adopted for applications in healthcare, reaching the performance of domain experts on tasks such as question answering and document su…
M-QALM: A Benchmark to Assess Clinical Reading Comprehension and Knowledge Recall in Large Language Models via Question Answering
Anand Subramanian, Viktor Schlegel, Abhinav Ramesh Kashyap +3
There is vivid research on adapting Large Language Models (LLMs) to perform a variety of tasks in high-stakes domains such as healthcare. Despite their popularity, there is a lack…
Which Side Are You On? A Multi-task Dataset for End-to-End Argument Summarisation and Evaluation
Hao Li, Yuping Wu, Viktor Schlegel +8
With the recent advances of large language models (LLMs), it is no longer infeasible to build an automated debate system that helps people to synthesise persuasive arguments. Previ…