6 papers
From Questions to Trust Reports: A LLM-IR Framework for the TREC 2025 DRAGUN Track
Ignacy Alwasiak, Kene Nnolim, Jaclyn Thi +5
The DRAGUN Track at TREC 2025 targets the growing need for effective support tools that help users evaluate the trustworthiness of online news. We describe the UR_Trecking system s…
MedNuggetizer: Confidence-Based Information Nugget Extraction from Medical Documents
Gregor Donabauer, Samy Ateia, Udo Kruschwitz +6
We present MedNuggetizer, https://mednugget-ai.de/; access is available upon request.}, a tool for query-driven extraction and clustering of information nuggets from medical docume…
LLM-Based Information Extraction to Support Scientific Literature Research and Publication Workflows
Samy Ateia, Udo Kruschwitz, Melanie Scholz +2
The increasing volume of scholarly publications requires advanced tools for efficient knowledge discovery and management. This paper introduces ongoing work on a system using Large…
Can Language Models Critique Themselves? Investigating Self-Feedback for Retrieval Augmented Generation at BioASQ 2025
Samy Ateia, Udo Kruschwitz
Agentic Retrieval Augmented Generation (RAG) and 'deep research' systems aim to enable autonomous search processes where Large Language Models (LLMs) iteratively refine outputs. Ho…
Query Smarter, Trust Better? Exploring Search Behaviours for Verifying News Accuracy
David Elsweiler, Samy Ateia, Markus Bink +8
While it is often assumed that searching for information to evaluate misinformation will help identify false claims, recent work suggests that search behaviours can instead reinfor…
BioRAGent: A Retrieval-Augmented Generation System for Showcasing Generative Query Expansion and Domain-Specific Search for Scientific Q&A
Samy Ateia, Udo Kruschwitz
We present BioRAGent, an interactive web-based retrieval-augmented generation (RAG) system for biomedical question answering. The system uses large language models (LLMs) for query…