@inproceedings{khan-etal-2026-df,
    title = "{DF}-{RAG}: Query-Aware Diversity for Retrieval-Augmented Generation",
    author = "Khan, Saadat Hasan  and
      Hong, Spencer  and
      Wu, Jingyu  and
      Lybarger, Kevin  and
      Yin, Youbing  and
      Babinsky, Erin  and
      Liu, Daben",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EACL} 2026",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.findings-eacl.150/",
    pages = "2873--2894",
    ISBN = "979-8-89176-386-9",
    abstract = "Retrieval-augmented generation (RAG) is a common technique for grounding language model outputs in domain-specific information. However, RAG is often challenged by reasoning-intensive question-answering (QA), since common retrieval methods like cosine similarity maximize relevance at the cost of introducing redundant content, which can reduce information recall. To address this, we introduce Diversity-Focused Retrieval-Augmented Generation (DF-RAG) that systematically incorporates diversity into the retrieval step to improve performance on complex, reasoning-intensive QA benchmarks. DF-RAG builds upon the Maximal Marginal Relevance framework to select information chunks that are both relevant to the query and maximally dissimilar from each other. A key innovation of DF-RAG is its ability to optimize the level of diversity for each query dynamically at test time without requiring any additional fine-tuning or prior information. We show that DF-RAG improves F1 performance on reasoning-intensive QA benchmarks by 4{--}10{\%} over vanilla RAG using cosine similarity and also outperforms other established baselines. Furthermore, we estimate an Oracle ceiling of up to 18{\%} absolute F1 gains over vanilla RAG, of which DF-RAG captures up to 91.3{\%}."
}