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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…
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…
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…
Information Redundancy and Biases in Public Document Information Extraction Benchmarks
Seif Laatiri, Pirashanth Ratnamogan, Joel Tang +3
Advances in the Visually-rich Document Understanding (VrDU) field and particularly the Key-Information Extraction (KIE) task are marked with the emergence of efficient Transformer-…
Information Extraction from Documents: Question Answering vs Token Classification in real-world setups
Laurent Lam, Pirashanth Ratnamogan, Joël Tang +2
Research in Document Intelligence and especially in Document Key Information Extraction (DocKIE) has been mainly solved as Token Classification problem. Recent breakthroughs in bot…