14 citations · 16 across the 9 of their papers we have counts for
10 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…
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…
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…
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…
Transformers as Graph-to-Graph Models
James Henderson, Alireza Mohammadshahi, Andrei C. Coman +1
We argue that Transformers are essentially graph-to-graph models, with sequences just being a special case. Attention weights are functionally equivalent to graph edges. Our Graph-…
Strong and Efficient Baselines for Open Domain Conversational Question Answering
Andrei C. Coman, Gianni Barlacchi, Adrià de Gispert
Unlike the Open Domain Question Answering (ODQA) setting, the conversational (ODConvQA) domain has received limited attention when it comes to reevaluating baselines for both effic…
GADePo: Graph-Assisted Declarative Pooling Transformers for Document-Level Relation Extraction
Andrei C. Coman, Christos Theodoropoulos, Marie-Francine Moens +1
Document-level relation extraction typically relies on text-based encoders and hand-coded pooling heuristics to aggregate information learned by the encoder. In this paper, we leve…
Imposing Relation Structure in Language-Model Embeddings Using Contrastive Learning
Christos Theodoropoulos, James Henderson, Andrei C. Coman +1
Though language model text embeddings have revolutionized NLP research, their ability to capture high-level semantic information, such as relations between entities in text, is lim…
An Incremental Turn-Taking Model For Task-Oriented Dialog Systems
Andrei C. Coman, Koichiro Yoshino, Yukitoshi Murase +2
In a human-machine dialog scenario, deciding the appropriate time for the machine to take the turn is an open research problem. In contrast, humans engaged in conversations are abl…