15 citations · 37 across the 13 of their papers we have counts for
5 papers · 1 filter
Provence: efficient and robust context pruning for retrieval-augmented generation
Nadezhda Chirkova, Thibault Formal, Vassilina Nikoulina +1
Retrieval-augmented generation improves various aspects of large language models (LLMs) generation, but suffers from computational overhead caused by long contexts as well as the p…
PISCO: Pretty Simple Compression for Retrieval-Augmented Generation
Maxime Louis, Hervé Déjean, Stéphane Clinchant
Retrieval-Augmented Generation (RAG) pipelines enhance Large Language Models (LLMs) by retrieving relevant documents, but they face scalability issues due to high inference costs a…
Efficient Inference for Multilingual Neural Machine Translation
Alexandre Berard, Dain Lee, Stéphane Clinchant +2
Multilingual NMT has become an attractive solution for MT deployment in production. But to match bilingual quality, it comes at the cost of larger and slower models. In this work,…
Masked Adversarial Generation for Neural Machine Translation
Badr Youbi Idrissi, Stéphane Clinchant
Attacking Neural Machine Translation models is an inherently combinatorial task on discrete sequences, solved with approximate heuristics. Most methods use the gradient to attack t…
On the use of BERT for Neural Machine Translation
Stéphane Clinchant, Kweon Woo Jung, Vassilina Nikoulina
Exploiting large pretrained models for various NMT tasks have gained a lot of visibility recently. In this work we study how BERT pretrained models could be exploited for supervise…