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

Alexandre Misrahi, Nadezhda Chirkova, Maxime Louis, Vassilina Nikoulina
4/3/2025
cs.CL

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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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Cite this paper

@article{misrahi2025adapting,
  title  = {Adapting Large Language Models for Multi-Domain Retrieval-Augmented-Generation},
  author = {Alexandre Misrahi and Nadezhda Chirkova and Maxime Louis and Vassilina Nikoulina},
  year   = {2025},
  eprint = {2504.02411},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2504.02411v1}
}

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