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…
Context Embeddings for Efficient Answer Generation in RAG
David Rau, Shuai Wang, Hervé Déjean +1
Retrieval-Augmented Generation (RAG) allows overcoming the limited knowledge of LLMs by extending the input with external information. As a consequence, the contextual inputs to th…
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…
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…