12 citations · 46 across the 25 of their papers we have counts for
6 papers · 2 filters
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…
FrenchToxicityPrompts: a Large Benchmark for Evaluating and Mitigating Toxicity in French Texts
Caroline Brun, Vassilina Nikoulina
Large language models (LLMs) are increasingly popular but are also prone to generating bias, toxic or harmful language, which can have detrimental effects on individuals and commun…
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.…