5 papers
LLM-based Generation of Semantically Diverse and Realistic Domain Model Instances
Andrei Coman, Lola Burgueño, Dominik Bork +1
Large Language Models (LLMs) have been recently proposed for supporting domain modeling tasks mostly related to the completion of partial models by recommending additional model el…
RAGferee: Building Contextual Reward Models for Retrieval-Augmented Generation
Andrei C. Coman, Ionut-Teodor Sorodoc, Leonardo F. R. Ribeiro +3
Existing Reward Models (RMs), typically trained on general preference data, struggle in Retrieval Augmented Generation (RAG) settings, which require judging responses for faithfuln…
Fast-and-Frugal Text-Graph Transformers are Effective Link Predictors
Andrei C. Coman, Christos Theodoropoulos, Marie-Francine Moens +1
We propose Fast-and-Frugal Text-Graph (FnF-TG) Transformers, a Transformer-based framework that unifies textual and structural information for inductive link prediction in text-att…
Reduction of Supervision for Biomedical Knowledge Discovery
Christos Theodoropoulos, Andrei Catalin Coman, James Henderson +1
Knowledge discovery is hindered by the increasing volume of publications and the scarcity of extensive annotated data. To tackle the challenge of information overload, it is essent…
Enhancing Biomedical Knowledge Discovery for Diseases: An Open-Source Framework Applied on Rett Syndrome and Alzheimer's Disease
Christos Theodoropoulos, Andrei Catalin Coman, James Henderson +1
The ever-growing volume of biomedical publications creates a critical need for efficient knowledge discovery. In this context, we introduce an open-source end-to-end framework desi…