Efficient Context Selection for Long-Context QA: No Tuning, No Iteration, Just Adaptive-$k$

• URL: http://arxiv.org/abs/2506.08479v1
• Date: Tue, 10 Jun 2025 06:11:01 GMT
• Title: Efficient Context Selection for Long-Context QA: No Tuning, No Iteration, Just Adaptive-$k$
• Authors: Chihiro Taguchi, Seiji Maekawa, Nikita Bhutani,
• Abstract summary: We present Adaptive-$k$, a simple and effective single-pass method that adaptively selects the number of passages based on the similarity scores between the query and the candidate passages.<n>It does not require model fine-tuning, extra LLM inferences or changes to existing retriever-reader pipelines.<n>It improves accuracy across five LCLMs and two embedding models, highlighting that dynamically adjusting context size leads to more efficient and accurate QA.
• Score: 5.026348938624301
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Retrieval-augmented generation (RAG) and long-context language models (LCLMs) both address context limitations of LLMs in open-domain question answering (QA). However, optimal external context to retrieve remains an open problem: fixing the retrieval size risks either 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 the optimal context size is both unknown and variable. We present Adaptive-$k$ retrieval, a simple and effective single-pass method that adaptively selects the number of passages based on the distribution of the similarity scores between the query and the candidate passages. It does not require model fine-tuning, extra LLM inferences 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, yet 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.

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