@inproceedings{chen-etal-2025-retrieve,
title = "Can we Retrieve Everything All at Once? {ARM}: An Alignment-Oriented {LLM}-based Retrieval Method",
author = "Chen, Peter Baile and
Zhang, Yi and
Cafarella, Mike and
Roth, Dan",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.1463/",
doi = "10.18653/v1/2025.acl-long.1463",
pages = "30298--30317",
ISBN = "979-8-89176-251-0",
abstract = "Real-world open-domain questions can be complex, especially when answering them requires integrating information from multiple sources. Effectively identifying the necessary information involves *aligning* it with the available data and its organization. However, existing RAG solutions address the alignment problem in a limited manner. Using off-the-shelf LLMs for question decomposition lacks awareness of the available data and its structure, often resulting in suboptimal retrieval performance. Alternatively, iteratively generating follow-up queries and interacting with the data collection, as explored in agentic RAG approaches, shows potential but is often *inefficient* since each successive query depends on previous results rather than being guided by the overall organization of the available data. To address the *alignment* problem, we introduce an LLM-based retrieval method {---} ARM, designed to better align questions with the organization of the data collection. Instead of solely matching query utterance, ARM explores *relationships among data objects*, enabling a retrieve-all-at-once solution for complex queries. Experimental results demonstrate that ARM significantly outperforms existing RAG methods on various complex open-domain QA tasks across multiple modalities, achieving superior retrieval performance and downstream accuracy while significantly lowering monetary costs."
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<abstract>Real-world open-domain questions can be complex, especially when answering them requires integrating information from multiple sources. Effectively identifying the necessary information involves *aligning* it with the available data and its organization. However, existing RAG solutions address the alignment problem in a limited manner. Using off-the-shelf LLMs for question decomposition lacks awareness of the available data and its structure, often resulting in suboptimal retrieval performance. Alternatively, iteratively generating follow-up queries and interacting with the data collection, as explored in agentic RAG approaches, shows potential but is often *inefficient* since each successive query depends on previous results rather than being guided by the overall organization of the available data. To address the *alignment* problem, we introduce an LLM-based retrieval method — ARM, designed to better align questions with the organization of the data collection. Instead of solely matching query utterance, ARM explores *relationships among data objects*, enabling a retrieve-all-at-once solution for complex queries. Experimental results demonstrate that ARM significantly outperforms existing RAG methods on various complex open-domain QA tasks across multiple modalities, achieving superior retrieval performance and downstream accuracy while significantly lowering monetary costs.</abstract>
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%0 Conference Proceedings
%T Can we Retrieve Everything All at Once? ARM: An Alignment-Oriented LLM-based Retrieval Method
%A Chen, Peter Baile
%A Zhang, Yi
%A Cafarella, Mike
%A Roth, Dan
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F chen-etal-2025-retrieve
%X Real-world open-domain questions can be complex, especially when answering them requires integrating information from multiple sources. Effectively identifying the necessary information involves *aligning* it with the available data and its organization. However, existing RAG solutions address the alignment problem in a limited manner. Using off-the-shelf LLMs for question decomposition lacks awareness of the available data and its structure, often resulting in suboptimal retrieval performance. Alternatively, iteratively generating follow-up queries and interacting with the data collection, as explored in agentic RAG approaches, shows potential but is often *inefficient* since each successive query depends on previous results rather than being guided by the overall organization of the available data. To address the *alignment* problem, we introduce an LLM-based retrieval method — ARM, designed to better align questions with the organization of the data collection. Instead of solely matching query utterance, ARM explores *relationships among data objects*, enabling a retrieve-all-at-once solution for complex queries. Experimental results demonstrate that ARM significantly outperforms existing RAG methods on various complex open-domain QA tasks across multiple modalities, achieving superior retrieval performance and downstream accuracy while significantly lowering monetary costs.
%R 10.18653/v1/2025.acl-long.1463
%U https://aclanthology.org/2025.acl-long.1463/
%U https://doi.org/10.18653/v1/2025.acl-long.1463
%P 30298-30317
Markdown (Informal)
[Can we Retrieve Everything All at Once? ARM: An Alignment-Oriented LLM-based Retrieval Method](https://aclanthology.org/2025.acl-long.1463/) (Chen et al., ACL 2025)
ACL