10 citations · 17 across the 5 of their papers we have counts for
7 papers
REDSI: Addressing the Reproducibility and Evaluation Consistency of Differentiable Search Indexing for Document Retrieval
Vivien Nicolas, Hicham Randrianarivo, Pascale Sébillot +1
The differentiable search index (DSI) framework (Tay et al., 2022) has become the de facto baseline for generative retrieval. However, DSI is hard to reproduce: no public implement…
LEDGER: A Long-Context Benchmark of Corporate Annual Reports for Grounded Financial Retrieval and Extraction
Charles Moslonka, Amaury de Vitry, Arthur Garnier +2
Finance reporting is a natural proving ground for large language models, and the very-long-context capabilities of recent models across all sizes make rigorous evaluation in this d…
Learned Hallucination Detection in Black-Box LLMs using Token-level Entropy Production Rate
Charles Moslonka, Hicham Randrianarivo, Arthur Garnier +1
Hallucinations in Large Language Model (LLM) outputs for Question Answering (QA) tasks can critically undermine their real-world reliability. This paper introduces a methodology fo…
Web Image Context Extraction with Graph Neural Networks and Sentence Embeddings on the DOM tree
Chen Dang, Hicham Randrianarivo, Raphaël Fournier-S'Niehotta +1
Web Image Context Extraction (WICE) consists in obtaining the textual information describing an image using the content of the surrounding webpage. A common preprocessing step befo…
Graphcore C2 Card performance for image-based deep learning application: A Report
Ilyes Kacher, Maxime Portaz, Hicham Randrianarivo +1
Recently, Graphcore has introduced an IPU Processor for accelerating machine learning applications. The architecture of the processor has been designed to achieve state of the art…
Weakly Supervised Semantic Segmentation of Satellite Images
Adrien Nivaggioli, Hicham Randrianarivo
When one wants to train a neural network to perform semantic segmentation, creating pixel-level annotations for each of the images in the database is a tedious task. If he works wi…