@inproceedings{taguchi-etal-2025-efficient,
    title = "Efficient Context Selection for Long-Context {QA}: No Tuning, No Iteration, Just Adaptive{-}$k$",
    author = "Taguchi, Chihiro  and
      Maekawa, Seiji  and
      Bhutani, Nikita",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/name-variant-enfa-fane/2025.emnlp-main.1017/",
    doi = "10.18653/v1/2025.emnlp-main.1017",
    pages = "20116--20141",
    ISBN = "979-8-89176-332-6",
    abstract = "Retrieval-augmented generation (RAG) and long-context language models (LCLMs) both address context limitations of LLMs in open-domain QA. However, optimal external context to retrieve remains an open problem: fixed retrieval budgets risk wasting tokens or omitting key evidence. Existing adaptive methods like Self-RAG and Self-Route rely on iterative LLM prompting and perform well on factoid QA, but struggle with aggregation QA where optimal context size is unknown and variable. We present Adaptive{-}$k$ retrieval, a simple and effective single-pass method that selects a query-specific number of passages by applying a threshold to the similarity scores between the query and candidate passages. It does not require model fine-tuning, extra LLM calls or changes to existing retriever{--}reader pipelines. On both factoid and aggregation QA benchmarks, Adaptive{-}$k$ matches or outperforms fixed{-}$k$ baselines while using up to 10x fewer tokens than full-context input, and still retrieves 70{\%} of relevant passages. It improves accuracy across five LCLMs and two embedding models, highlighting that dynamically adjusting context size leads to more efficient and accurate QA."
}