4 papers · 1 filter
NeSy-RAG: Neuro-Symbolic RAG for Explainable Question Answering
Jonas Gann, Michael Gertz
Retrieval-augmented generation (RAG) improves question answering by grounding large language models (LLMs) in external knowledge such as text corpora. However, its reasoning proces…
From Answers to Guidance: A Proactive Dialogue System for Legal Documents
Ashish Chouhan, Michael Gertz
The accessibility of legal information remains a constant challenge, particularly for laypersons seeking to understand and apply complex institutional texts. While the European Uni…
heiDS at ArchEHR-QA 2025: From Fixed-k to Query-dependent-k for Retrieval Augmented Generation
Ashish Chouhan, Michael Gertz
This paper presents the approach of our team called heiDS for the ArchEHR-QA 2025 shared task. A pipeline using a retrieval augmented generation (RAG) framework is designed to gene…
ClusterChat: Multi-Feature Search for Corpus Exploration
Ashish Chouhan, Saifeldin Mandour, Michael Gertz
Exploring large-scale text corpora presents a significant challenge in biomedical, finance, and legal domains, where vast amounts of documents are continuously published. Tradition…