Adapting Large Language Models for Multi-Domain Retrieval-Augmented-Generation

Alexandre Misrahi; Nadezhda Chirkova; Maxime Louis; Vassilina Nikoulina

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arXiv · 2504.02411

Adapting Large Language Models for Multi-Domain Retrieval-Augmented-Generation

Alexandre Misrahi·Nadezhda Chirkova·Maxime Louis·Vassilina Nikoulina

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Abstract

Retrieval-Augmented Generation (RAG) enhances LLM factuality, but multi-domain applications face challenges like lack of diverse benchmarks and poor out-of-domain generalization. The first contribution of this work is to introduce a diverse benchmark comprising a variety of question-answering tasks from 8 sources and covering 13 domains. Our second contribution consists in systematically testing out-of-domain generalization for typical RAG tuning strategies. While our findings reveal that standard fine-tuning fails to generalize effectively, we show that sequence-level distillation with teacher-generated labels improves out-of-domain performance by providing more coherent supervision. Our findings highlight key strategies for improving multi-domain RAG robustness.

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Alexandre Misrahi, Nadezhda Chirkova, Maxime Louis, Vassilina Nikoulina. 2025-04-03. Adapting Large Language Models for Multi-Domain Retrieval-Augmented-Generation. https://arxiv.org/abs/2504.02411

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