Year: 2026
Type: conference-paper
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 surfaceform generation amplifies rather than corrects underlying fact representations.We also introduce UnFine, a diagnostic benchmark with fine-grained question-answer pairs and factlevel metrics for systematic evaluation.Experiments show that FABLE substantially improves fine-grained question answering while maintaining state-of-the-art holistic editing performance.
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Award: G5208670511
OpenAlex ID: W7166815109