@inproceedings{wang-etal-2026-elementcheck,
title = "{E}lement{C}heck: Complexity-Aware Long-Form Text Factuality Evaluation via Sentence Elements",
author = "Wang, Xinming and
Du, Haoran and
Chen, Yi and
Chen, Xueqing and
Xu, Jian and
Yang, Hong-Ming and
Hu, Han and
Chen, Yulong and
Liu, Cheng-Lin and
Zhang, Xu-Yao",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.868/",
pages = "18336--18363",
ISBN = "979-8-89176-462-0",
abstract = "Existing long-form factuality evaluation relies on the decompose-retrieve-verify pipeline. However, the pipeline suffers from noise from claim decomposition and fixed verification granularity, resulting in unreliable results. We propose ElementCheck, a complexity-aware framework that verifies long-form outputs via sentence elements. Instead of uniformly decomposing sentences into atomic sub-claims, ElementCheck extracts entity pairs that are explicitly linked through verifiable connections in the original sentence as elements, and organizes these into an element graph. The graph topology provides a structural signal for estimating sentence complexity, enabling direct verification for simple sentences and targeted element-level refinement and verification for complex ones. To support fine-grained evaluation, we construct a new benchmark FastFact-Sent by mapping isolated claims from FastFact-Bench back to their source sentences. Experiments on FastFact-Sent and two domain-specific benchmarks show ElementCheck consistently improves factuality verification across five backbone models while maintaining a favorable accuracy-cost trade-off. Further analyses demonstrate that complexity-aware verification reduces unnecessary re-verification and maintains stability across different backbones. The code is available at \href{https://github.com/gudehhh666/elementcheck.git}{Here}."
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<abstract>Existing long-form factuality evaluation relies on the decompose-retrieve-verify pipeline. However, the pipeline suffers from noise from claim decomposition and fixed verification granularity, resulting in unreliable results. We propose ElementCheck, a complexity-aware framework that verifies long-form outputs via sentence elements. Instead of uniformly decomposing sentences into atomic sub-claims, ElementCheck extracts entity pairs that are explicitly linked through verifiable connections in the original sentence as elements, and organizes these into an element graph. The graph topology provides a structural signal for estimating sentence complexity, enabling direct verification for simple sentences and targeted element-level refinement and verification for complex ones. To support fine-grained evaluation, we construct a new benchmark FastFact-Sent by mapping isolated claims from FastFact-Bench back to their source sentences. Experiments on FastFact-Sent and two domain-specific benchmarks show ElementCheck consistently improves factuality verification across five backbone models while maintaining a favorable accuracy-cost trade-off. Further analyses demonstrate that complexity-aware verification reduces unnecessary re-verification and maintains stability across different backbones. The code is available at \hrefhttps://github.com/gudehhh666/elementcheck.gitHere.</abstract>
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%0 Conference Proceedings
%T ElementCheck: Complexity-Aware Long-Form Text Factuality Evaluation via Sentence Elements
%A Wang, Xinming
%A Du, Haoran
%A Chen, Yi
%A Chen, Xueqing
%A Xu, Jian
%A Yang, Hong-Ming
%A Hu, Han
%A Chen, Yulong
%A Liu, Cheng-Lin
%A Zhang, Xu-Yao
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F wang-etal-2026-elementcheck
%X Existing long-form factuality evaluation relies on the decompose-retrieve-verify pipeline. However, the pipeline suffers from noise from claim decomposition and fixed verification granularity, resulting in unreliable results. We propose ElementCheck, a complexity-aware framework that verifies long-form outputs via sentence elements. Instead of uniformly decomposing sentences into atomic sub-claims, ElementCheck extracts entity pairs that are explicitly linked through verifiable connections in the original sentence as elements, and organizes these into an element graph. The graph topology provides a structural signal for estimating sentence complexity, enabling direct verification for simple sentences and targeted element-level refinement and verification for complex ones. To support fine-grained evaluation, we construct a new benchmark FastFact-Sent by mapping isolated claims from FastFact-Bench back to their source sentences. Experiments on FastFact-Sent and two domain-specific benchmarks show ElementCheck consistently improves factuality verification across five backbone models while maintaining a favorable accuracy-cost trade-off. Further analyses demonstrate that complexity-aware verification reduces unnecessary re-verification and maintains stability across different backbones. The code is available at \hrefhttps://github.com/gudehhh666/elementcheck.gitHere.
%U https://aclanthology.org/2026.findings-emnlp.868/
%P 18336-18363
Markdown (Informal)
[ElementCheck: Complexity-Aware Long-Form Text Factuality Evaluation via Sentence Elements](https://aclanthology.org/2026.findings-emnlp.868/) (Wang et al., Findings 2026)
ACL
- Xinming Wang, Haoran Du, Yi Chen, Xueqing Chen, Jian Xu, Hong-Ming Yang, Han Hu, Yulong Chen, Cheng-Lin Liu, and Xu-Yao Zhang. 2026. ElementCheck: Complexity-Aware Long-Form Text Factuality Evaluation via Sentence Elements. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 18336–18363, Budapest, Hungary. Association for Computational Linguistics.