Tag
This paper introduces HPSE, a hybrid-policy self-editing method for composable unstructured knowledge editing in LLMs, enabling edited models to answer atomic questions and perform multi-hop reasoning about injected knowledge. The approach uses proactive self-distillation with hybrid rollouts and is validated across multiple LLM backbones and editors.
ForgetBench introduces a benchmark to systematically characterize forgetting behavior in large language models under continual knowledge editing, using concept-based and scenario-based QA to measure temporal decay and retention dynamics.
Moir is a method that improves cross-domain knowledge editing in LLMs by aligning the preservation distribution with the model's own decoding distribution, avoiding reliance on external corpora. It consistently preserves complex capabilities like mathematical reasoning across multiple models and editors.
KnowledgeDebugger is a GUI-based tool for exploring knowledge localization and editing in Transformer models, integrating state-of-the-art knowledge editing methods from the EasyEdit library to support intuitive analysis and hypothesis formation.
This paper identifies and addresses the 'editing decoupling failure' in Multimodal LLMs, where knowledge updates via multimodal inputs fail to generalize to unimodal queries. The authors propose DECODE, a method to disentangle and localize modality-specific neurons for more effective knowledge editing.
Introduces a benchmark to evaluate how knowledge editing methods handle logical consequences of fact edits, revealing that existing approaches like ROME and FT accurately insert direct assertions but fail to propagate entailed knowledge, with a performance gap of up to 24%.
HyperPatch proposes a parameter-preserving framework for sequential knowledge editing under n-ary structural drift, using hypergraph neural networks to maintain event integrity. It achieves 96.24% and 21.06% relative improvements in Hop-wise Accuracy on MQuAKE-CF and MQuAKE-T benchmarks, respectively.
This paper introduces the FactualOpinionEditing with Evidence (FOE) benchmark to assess the ability to edit factual opinions in LLMs, and proposes a Self-Generated Evidence-Aligned method to improve opinion-evidence alignment.
This paper presents a theoretical analysis of parameter-based knowledge editing in LLMs, revealing how localized edits can cause global reasoning collapse, and provides empirical evidence that retrieval-based methods outperform parameter-editing approaches.
This paper introduces UniKE, the first benchmark for cross-modal knowledge editing in unified multimodal models (UMMs), revealing a significant modality gap where text edits achieve 92% efficacy but only 18.5% transfer to image generation. It proposes Reasoning-augmented Parameter Editing to improve cross-modal transfer, with gains up to 18.6 percentage points.
This paper investigates the internal mechanisms of knowledge editing methods ROME and MEMIT, revealing that edits rely on a common functional subspace of weights and suppress rather than overwrite knowledge, explaining why edits fail to propagate to related facts.
This paper investigates the mechanisms underlying sequential knowledge editing in LLMs, showing that many regularization strategies are unnecessary and that stability emerges naturally from properly accounting for accumulated editing constraints.
This paper introduces ASAM (Adversarial Subspace Alignment for Multimodal Knowledge Editing), a method that improves the generality of intrinsic multimodal knowledge editing by generating adversarial yet semantically coherent variants and enforcing low-rank alignment of representations, addressing the limited generalization of existing methods.
This paper empirically evaluates vector merging methods for multilingual knowledge editing in large language models, identifying vector summation with shared covariance as the most reliable strategy and highlighting the limited effectiveness of Task Singular Vectors for Merging (TSVM) in reducing multilingual interference.
This paper identifies a failure mode called Entity Identity Confusion in multimodal knowledge editing, where models incorrectly bind image-entity relationships. It introduces EC-Bench to diagnose this issue and proposes mitigation strategies for faithful editing.