@inproceedings{li-etal-2026-facta,
title = "{FACTA}: Structured Fact Alignment for Multi-Hop Retrieval-Augmented Generation",
author = "Li, Jiahao and
Xu, Yongxiu and
Zhang, Xumeng and
Xu, Hongbo and
Tang, Minghao and
Wang, Yubin and
Gou, Gaopeng",
editor = "Zhang, Yue and
Xu, Ruifeng and
Purwarianti, Ayu and
Rong, Tong and
Suzuki, Jun",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {IJCNLP}-{AACL} 2026",
month = nov,
year = "2026",
address = "Hengqin, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-aacl.90/",
pages = "1815--1831",
ISBN = "979-8-89176-444-6",
abstract = "Although Retrieval-Augmented Generation (RAG) empowers Large Language Models (LLMs) with external knowledge, current frameworks struggle with complex multi-hop reasoning as they fundamentally rely on shallow retrieval signals. Existing methods typically employ coarse semantic matching or entity-centric graph expansion, which often retrieve textually similar but factually irrelevant evidence, injecting noise into the reasoning process. To overcome this limitation, we introduce FACTA, a structured fact alignment framework for multi-hop retrieval. FACTA bridges the gap between a query{'}s step-wise information demand and a document{'}s information supply by translating both into structured, explicit factual triples. Through a dual mechanism of triple-level fact matching and multi-signal document reranking, FACTA better identifies evidence that satisfies the factual prerequisites of each reasoning hop. Extensive evaluations on three multi-hop QA benchmarks show that FACTA substantially outperforms state-of-the-art baselines, demonstrating that structured fact-centric retrieval effectively reduces reasoning noise and enhances the accuracy of multi-hop generation."
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<abstract>Although Retrieval-Augmented Generation (RAG) empowers Large Language Models (LLMs) with external knowledge, current frameworks struggle with complex multi-hop reasoning as they fundamentally rely on shallow retrieval signals. Existing methods typically employ coarse semantic matching or entity-centric graph expansion, which often retrieve textually similar but factually irrelevant evidence, injecting noise into the reasoning process. To overcome this limitation, we introduce FACTA, a structured fact alignment framework for multi-hop retrieval. FACTA bridges the gap between a query’s step-wise information demand and a document’s information supply by translating both into structured, explicit factual triples. Through a dual mechanism of triple-level fact matching and multi-signal document reranking, FACTA better identifies evidence that satisfies the factual prerequisites of each reasoning hop. Extensive evaluations on three multi-hop QA benchmarks show that FACTA substantially outperforms state-of-the-art baselines, demonstrating that structured fact-centric retrieval effectively reduces reasoning noise and enhances the accuracy of multi-hop generation.</abstract>
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%0 Conference Proceedings
%T FACTA: Structured Fact Alignment for Multi-Hop Retrieval-Augmented Generation
%A Li, Jiahao
%A Xu, Yongxiu
%A Zhang, Xumeng
%A Xu, Hongbo
%A Tang, Minghao
%A Wang, Yubin
%A Gou, Gaopeng
%Y Zhang, Yue
%Y Xu, Ruifeng
%Y Purwarianti, Ayu
%Y Rong, Tong
%Y Suzuki, Jun
%S Findings of the Association for Computational Linguistics: IJCNLP-AACL 2026
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, China
%@ 979-8-89176-444-6
%F li-etal-2026-facta
%X Although Retrieval-Augmented Generation (RAG) empowers Large Language Models (LLMs) with external knowledge, current frameworks struggle with complex multi-hop reasoning as they fundamentally rely on shallow retrieval signals. Existing methods typically employ coarse semantic matching or entity-centric graph expansion, which often retrieve textually similar but factually irrelevant evidence, injecting noise into the reasoning process. To overcome this limitation, we introduce FACTA, a structured fact alignment framework for multi-hop retrieval. FACTA bridges the gap between a query’s step-wise information demand and a document’s information supply by translating both into structured, explicit factual triples. Through a dual mechanism of triple-level fact matching and multi-signal document reranking, FACTA better identifies evidence that satisfies the factual prerequisites of each reasoning hop. Extensive evaluations on three multi-hop QA benchmarks show that FACTA substantially outperforms state-of-the-art baselines, demonstrating that structured fact-centric retrieval effectively reduces reasoning noise and enhances the accuracy of multi-hop generation.
%U https://aclanthology.org/2026.findings-aacl.90/
%P 1815-1831
Markdown (Informal)
[FACTA: Structured Fact Alignment for Multi-Hop Retrieval-Augmented Generation](https://aclanthology.org/2026.findings-aacl.90/) (Li et al., Findings 2026)
ACL