%Aigaion2 BibTeX export from Idiap Publications
%Friday 05 December 2025 03:45:42 PM

@INPROCEEDINGS{Coman_EMNLP_2025,
                      author = {Coman, Andrei Catalin and Sorodoc, Ionuț-Teodor and Ribeiro, Leonardo F. R. and Byrne, Bill and Henderson, James and de Gispert, Adri{\`{a}}},
                    projects = {Idiap, NKBP},
                       title = {RAGferee: Building Contextual Reward Models for Retrieval-Augmented Generation},
                   booktitle = {Empirical Methods in Natural Language Processing},
                        year = {2025},
                         url = {https://www.arxiv.org/abs/2509.26011},
                    abstract = {Existing Reward Models (RMs), typically trained on general preference data, struggle in Retrieval Augmented Generation (RAG) settings, which require judging responses for faithfulness to retrieved context, relevance to the user query, appropriate refusals when context is insufficient, completeness and conciseness of information. To address the lack of publicly available RAG-centric preference datasets and specialised RMs, we introduce RAGferee, a methodology that repurposes question-answering (QA) datasets into preference pairs that prioritise groundedness over stylistic features, enabling the training of contextual RMs better suited to judging RAG responses. Using RAGferee, we curate a small preference dataset of 4K samples and fine-tune RMs ranging from 7B to 24B parameters. Our RAG-centric RMs achieve state-of-the-art performance on ContextualJudgeBench, surpassing existing 70B+ RMs trained on much larger (up to 2.4M samples) general corpora, with an absolute improvement of +15.5\%.},
                         pdf = {https://publications.idiap.ch/attachments/papers/2025/Coman_EMNLP_2025.pdf}
}