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cs.CL2025
Towards Lighter and Robust Evaluation for Retrieval Augmented Generation
Alex-Razvan Ispas, Charles-Elie Simon, Fabien Caspani +1
Large Language Models are prompting us to view more NLP tasks from a generative perspective. At the same time, they offer a new way of accessing information, mainly through the RAG…
cs.CL2025
Passage Segmentation of Documents for Extractive Question Answering
Zuhong Liu, Charles-Elie Simon, Fabien Caspani
Retrieval-Augmented Generation (RAG) has proven effective in open-domain question answering. However, the chunking process, which is essential to this pipeline, often receives insu…
cs.CL2024
A LayoutLMv3-Based Model for Enhanced Relation Extraction in Visually-Rich Documents
Wiam Adnan, Joel Tang, Yassine Bel Khayat Zouggari +3
Document Understanding is an evolving field in Natural Language Processing (NLP). In particular, visual and spatial features are essential in addition to the raw text itself and he…