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        <title>How You Ask Matters! Adaptive RAG Robustness to Query Variations</title>
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    <abstract>Adaptive Retrieval-Augmented Generation (RAG) promises accuracy and efficiency by dynamically triggering retrieval only when needed and is widely used in practice. However, real-world queries vary in surface form even with the same intent, and their impact on Adaptive RAG remains under-explored. We introduce the first large-scale benchmark of diverse yet semantically identical query variations, combining human-written and model-generated rewrites. Our benchmark facilitates a systematic evaluation of Adaptive RAG robustness by examining its key components across three dimensions: answer quality, computational cost, and retrieval decisions. We discover a critical robustness gap, where small surface-level changes in queries dramatically alter retrieval behavior and accuracy. Although larger models show better performance, robustness does not improve accordingly. These findings reveal that Adaptive RAG methods are highly vulnerable to query variations that preserve identical semantics, exposing a critical robustness challenge.</abstract>
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%0 Conference Proceedings
%T How You Ask Matters! Adaptive RAG Robustness to Query Variations
%A Jang, Yunah
%A Sundriyal, Megha
%A Jung, Kyomin
%A Cha, Meeyoung
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F jang-etal-2026-ask
%X Adaptive Retrieval-Augmented Generation (RAG) promises accuracy and efficiency by dynamically triggering retrieval only when needed and is widely used in practice. However, real-world queries vary in surface form even with the same intent, and their impact on Adaptive RAG remains under-explored. We introduce the first large-scale benchmark of diverse yet semantically identical query variations, combining human-written and model-generated rewrites. Our benchmark facilitates a systematic evaluation of Adaptive RAG robustness by examining its key components across three dimensions: answer quality, computational cost, and retrieval decisions. We discover a critical robustness gap, where small surface-level changes in queries dramatically alter retrieval behavior and accuracy. Although larger models show better performance, robustness does not improve accordingly. These findings reveal that Adaptive RAG methods are highly vulnerable to query variations that preserve identical semantics, exposing a critical robustness challenge.
%U https://aclanthology.org/2026.emnlp-main.2156/
%P 49039-49077

Markdown (Informal)

[How You Ask Matters! Adaptive RAG Robustness to Query Variations](https://aclanthology.org/2026.emnlp-main.2156/) (Jang et al., EMNLP 2026)

• How You Ask Matters! Adaptive RAG Robustness to Query Variations (Jang et al., EMNLP 2026)

ACL

• Yunah Jang, Megha Sundriyal, Kyomin Jung, and Meeyoung Cha. 2026. How You Ask Matters! Adaptive RAG Robustness to Query Variations. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 49039–49077, Budapest, Hungary. Association for Computational Linguistics.