@inproceedings{xu-etal-2025-simrag,
    title = "{S}im{RAG}: Self-Improving Retrieval-Augmented Generation for Adapting Large Language Models to Specialized Domains",
    author = "Xu, Ran  and
      Liu, Hui  and
      Nag, Sreyashi  and
      Dai, Zhenwei  and
      Xie, Yaochen  and
      Tang, Xianfeng  and
      Luo, Chen  and
      Li, Yang  and
      Ho, Joyce C.  and
      Yang, Carl  and
      He, Qi",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.naacl-long.575/",
    pages = "11534--11550",
    ISBN = "979-8-89176-189-6",
    abstract = "Retrieval-augmented generation (RAG) enhances the question answering (QA) abilities of large language models (LLMs) by integrating external knowledge. However, adapting general-purpose RAG systems to specialized fields such as science and medicine poses unique challenges due to distribution shifts and limited access to domain-specific data. To tackle this, we propose SimRAG, a self-training approach that equips LLMs with joint capabilities of question answering and question generation for domain adaptation. Our method first fine-tunes LLMs on instruction-following, question-answering, and search-related data. Then, it prompts LLMs to generate diverse domain-relevant questions from unlabeled corpora, with an additional filtering strategy to retain high-quality synthetic examples. By leveraging these synthetic examples, the LLMs can improve their performance on domain-specific RAG tasks. Experiments on 11 datasets across three different domains verify the efficacy of SimRAG over baselines by 1.2{\%}{--}8.6{\%}."
}