FABLE: Fine-grained Fact Anchoring for Unstructured Model Editing

• URL: http://arxiv.org/abs/2604.12559v1
• Date: Tue, 14 Apr 2026 10:41:36 GMT
• Title: FABLE: Fine-grained Fact Anchoring for Unstructured Model Editing
• Abstract summary: Unstructured model editing aims to update models with real-world text, yet existing methods often memorize text holistically without reliable fine-grained fact access.<n>We propose FABLE, a hierarchical framework that decouples fine-grained fact injection from holistic text generation.<n>Experiments show that FABLE substantially improves fine-grained question answering while maintaining state-of-the-art holistic editing performance.
• Score: 28.870053452479443
• License: http://creativecommons.org/licenses/by/4.0/
• 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://github.com/caskcsg/FABLE.

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