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        <title>Logic Matters in Lightweight Hallucination Classification for RAG System</title>
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        <namePart type="given">Ningyuan</namePart>
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            <namePart type="given">Jiajun</namePart>
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    <abstract>We propose a lightweight, modular framework for hallucination detection in Retrieval-Augmented Generation (RAG) systems, addressing the critical challenge where logical dependencies span across fragmented retrieval results. To address the inherent limitations of compact models in processing long-context information and performing multi-hop reasoning, our approach systematically analyzes the logical relationships among retrieved documents within the vector space. By capturing these geometric patterns through a novel feature extraction framework, the proposed classifier significantly enhances context-aware hallucination detection without requiring complex architectures or pre-training on datasets. Meanwhile, to evaluate multi-document reasoning, we release HotPotQA-derived, a hallucination dataset preserving separate retrieved texts. Experimental results on HotPotQA-derived and several open-source datasets demonstrate that our framework can achieve results comparable to or even surpassing those of large language models (LLMs) on the task of hallucination detection.</abstract>
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%0 Conference Proceedings
%T Logic Matters in Lightweight Hallucination Classification for RAG System
%A Yang, Ningyuan
%A Huang, Kaizhu
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F yang-huang-2026-logic
%X We propose a lightweight, modular framework for hallucination detection in Retrieval-Augmented Generation (RAG) systems, addressing the critical challenge where logical dependencies span across fragmented retrieval results. To address the inherent limitations of compact models in processing long-context information and performing multi-hop reasoning, our approach systematically analyzes the logical relationships among retrieved documents within the vector space. By capturing these geometric patterns through a novel feature extraction framework, the proposed classifier significantly enhances context-aware hallucination detection without requiring complex architectures or pre-training on datasets. Meanwhile, to evaluate multi-document reasoning, we release HotPotQA-derived, a hallucination dataset preserving separate retrieved texts. Experimental results on HotPotQA-derived and several open-source datasets demonstrate that our framework can achieve results comparable to or even surpassing those of large language models (LLMs) on the task of hallucination detection.
%U https://aclanthology.org/2026.acl-long.73/
%P 1605-1617

Markdown (Informal)

[Logic Matters in Lightweight Hallucination Classification for RAG System](https://aclanthology.org/2026.acl-long.73/) (Yang & Huang, ACL 2026)

• Logic Matters in Lightweight Hallucination Classification for RAG System (Yang & Huang, ACL 2026)

ACL

• Ningyuan Yang and Kaizhu Huang. 2026. Logic Matters in Lightweight Hallucination Classification for RAG System. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1605–1617, San Diego, California, United States. Association for Computational Linguistics.