Papers by Andrei Catalin Coman
Fast-and-Frugal Text-Graph Transformers are Effective Link Predictors (2025.findings-acl)
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| Challenge: | Existing methods that encode textual and structural information for inductive link prediction are frugal and fast at training and inference time. |
| Approach: | They propose a Transformer-based framework that unifies textual and structural information for inductive link prediction in text-attributed knowledge graphs by encoding ego-graphs (1-hop neighbourhoods). |
| Outcome: | The proposed framework can achieve superior performance on three popular datasets and reduce the reliance on resource-intensive encoders. |
RAGferee: Building Contextual Reward Models for Retrieval-Augmented Generation (2025.emnlp-main)
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Andrei Catalin Coman, Ionut Teodor Sorodoc, Leonardo F. R. Ribeiro, Bill Byrne, James Henderson, Adrià de Gispert
| Challenge: | Existing Reward Models (RMs) struggle in Retrieval Augmented Generation settings. |
| Approach: | They propose a method that repurposes question-answering datasets into preference pairs that prioritise groundedness over stylistic features. |
| Outcome: | The proposed method surpasses existing RMs trained on larger general corpora with an absolute improvement of +15.5%. |