TangleScore: Tangle-Guided Purge and Imprint for Unstructured Knowledge Editing

ICLR 2026 Conference Submission10125 Authors

Keywords: TangleScore, Unstructured Knowledge Editing, LLMs, Purge and Imprint

TL;DR: This paper introduces TangleScore to assess the editability of unstructured knowledge and proposes PIPE to handle knowledge with varying levels of editing difficulty.

Abstract: Large language models (LLMs) struggle with inaccurate and outdated information, driving the emergence of knowledge editing as a lightweight alternative. Despite their effectiveness in modifying structured knowledge, existing editing methods often fail to generalize to unstructured cases, particularly those involving inherently hard-to-edit knowledge, where the original facts tend to be more resistant to change. To address this, we propose a metric, TangleScore, that quantifies the intrinsic difficulty of editing a given knowledge instance. This difficulty, in turn, strongly correlates with the model’s ability to generalize the edit to paraphrased and related prompts. Building on this insight, we introduce a TangleScore-driven method termed Purge-Imprint Patch Editing (PIPE), an editing framework that adaptively modulates the purge and imprint of knowledge based on TangleScore of the target knowledge to be edited, thus adjusting the editing strength to match the instance's difficulty, thereby enabling more precise and effective model updates. Experiments applying PIPE to four LLMs of varying sizes on two unstructured knowledge editing datasets show that PIPE significantly outperforms previous editing methods by 6.49% in terms of generalization performance. Extensive evaluation show that PIPE also exhibits effectiveness in structured knowledge editing and strong robustness under batch and sequential editing.

Primary Area: transfer learning, meta learning, and lifelong learning

Submission Number: 10125