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    <abstract>Unstructured model editing aims to update models with real-world text, yet existing methods often memorize text holistically without reliable fine-grained fact access. To address this, we propose FABLE, a hierarchical framework that decouples fine-grained fact injection from holistic text generation. FABLE follows a two-stage, fact-first strategy: discrete facts are anchored in shallow layers, followed by minimal updates to deeper layers to produce coherent text. This decoupling resolves the mismatch between holistic recall and fine-grained fact access, reflecting the unidirectional Transformer flow in which surface-form generation amplifies rather than corrects underlying fact representations. We also introduce UnFine, a diagnostic benchmark with fine-grained question–answer pairs and fact-level metrics for systematic evaluation. Experiments show that FABLE substantially improves fine-grained question answering while maintaining state-of-the-art holistic editing performance. Our code is publicly available at https://anonymous.4open.science/r/FABLE-B59E.</abstract>
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%0 Conference Proceedings
%T FABLE: Fine-grained Fact Anchoring for Unstructured Model Editing
%A Wang, Peng
%A Zhou, Biyu
%A Tang, Xuehai
%A Han, Jizhong
%A Hu, Songlin
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F wang-etal-2026-fable
%X Unstructured model editing aims to update models with real-world text, yet existing methods often memorize text holistically without reliable fine-grained fact access. To address this, we propose FABLE, a hierarchical framework that decouples fine-grained fact injection from holistic text generation. FABLE follows a two-stage, fact-first strategy: discrete facts are anchored in shallow layers, followed by minimal updates to deeper layers to produce coherent text. This decoupling resolves the mismatch between holistic recall and fine-grained fact access, reflecting the unidirectional Transformer flow in which surface-form generation amplifies rather than corrects underlying fact representations. We also introduce UnFine, a diagnostic benchmark with fine-grained question–answer pairs and fact-level metrics for systematic evaluation. Experiments show that FABLE substantially improves fine-grained question answering while maintaining state-of-the-art holistic editing performance. Our code is publicly available at https://anonymous.4open.science/r/FABLE-B59E.
%U https://aclanthology.org/2026.findings-acl.714/
%P 14549-14567

Markdown (Informal)

[FABLE: Fine-grained Fact Anchoring for Unstructured Model Editing](https://aclanthology.org/2026.findings-acl.714/) (Wang et al., Findings 2026)

• FABLE: Fine-grained Fact Anchoring for Unstructured Model Editing (Wang et al., Findings 2026)

ACL

• Peng Wang, Biyu Zhou, Xuehai Tang, Jizhong Han, and Songlin Hu. 2026. FABLE: Fine-grained Fact Anchoring for Unstructured Model Editing. In Findings of the Association for Computational Linguistics: ACL 2026, pages 14549–14567, San Diego, California, United States. Association for Computational Linguistics.