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

• URL: http://arxiv.org/abs/2504.02411v1
• Date: Thu, 03 Apr 2025 09:03:40 GMT
• Title: Adapting Large Language Models for Multi-Domain Retrieval-Augmented-Generation
• Authors: Alexandre Misrahi, Nadezhda Chirkova, Maxime Louis, Vassilina Nikoulina,
• Abstract summary: Multi-domain applications face challenges like lack of diverse benchmarks and poor out-of-domain generalization.<n>We introduce a diverse benchmark comprising a variety of question-answering tasks from 8 sources and covering 13 domains.<n>Our findings highlight key strategies for improving multi-domain RAG robustness.
• Score: 59.58987161199141
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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