2 citations · 4 across the 10 of their papers we have counts for
10 papers
XProvence: Zero-Cost Multilingual Context Pruning for Retrieval-Augmented Generation
Youssef Mohamed, Mohamed Elhoseiny, Thibault Formal +1
This paper introduces XProvence, a multilingual zero-cost context pruning model for retrieval-augmented generation (RAG), trained on 16 languages and supporting 100+ languages thro…
DiffLoRA: Differential Low-Rank Adapters for Large Language Models
Alexandre Misrahi, Nadezhda Chirkova, Maxime Louis +1
Differential Transformer has recently been proposed to improve performance in Transformer models by canceling out noise through a denoiser attention mechanism. In this work, we int…
Adapting Large Language Models for Multi-Domain Retrieval-Augmented-Generation
Alexandre Misrahi, Nadezhda Chirkova, Maxime Louis +1
Retrieval-Augmented Generation (RAG) enhances LLM factuality, but multi-domain applications face challenges like lack of diverse benchmarks and poor out-of-domain generalization. T…
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…
Retrieval-augmented generation in multilingual settings
Nadezhda Chirkova, David Rau, Hervé Déjean +3
Retrieval-augmented generation (RAG) has recently emerged as a promising solution for incorporating up-to-date or domain-specific knowledge into large language models (LLMs) and im…
Investigating the potential of Sparse Mixtures-of-Experts for multi-domain neural machine translation
Nadezhda Chirkova, Vassilina Nikoulina, Jean-Luc Meunier +1
We focus on multi-domain Neural Machine Translation, with the goal of developing efficient models which can handle data from various domains seen during training and are robust to…
BERGEN: A Benchmarking Library for Retrieval-Augmented Generation
David Rau, Hervé Déjean, Nadezhda Chirkova +4
Retrieval-Augmented Generation allows to enhance Large Language Models with external knowledge. In response to the recent popularity of generative LLMs, many RAG approaches have be…
Zero-shot cross-lingual transfer in instruction tuning of large language models
Nadezhda Chirkova, Vassilina Nikoulina
Instruction tuning (IT) is widely used to teach pretrained large language models (LLMs) to follow arbitrary instructions, but is under-studied in multilingual settings. In this wor…
Key ingredients for effective zero-shot cross-lingual knowledge transfer in generative tasks
Nadezhda Chirkova, Vassilina Nikoulina
Zero-shot cross-lingual knowledge transfer enables a multilingual pretrained language model, finetuned on a task in one language, make predictions for this task in other languages.…
Empirical study of pretrained multilingual language models for zero-shot cross-lingual knowledge transfer in generation
Nadezhda Chirkova, Sheng Liang, Vassilina Nikoulina
Zero-shot cross-lingual knowledge transfer enables the multilingual pretrained language model (mPLM), finetuned on a task in one language, make predictions for this task in other l…