arXiv:2606. 19679v1 Announce Type: cross Abstract: Lifelong knowledge editing aims to efficiently and sequentially update language models over time, as new knowledge becomes available or when the model makes mistakes, while preserving acceptable performance on past knowledge.
arXiv:2603.12677v4 Announce Type: replace-cross Abstract: Existing locate-then-edit Knowledge Editing (KE) methods typically decompose editing into two stages: upstream target representation optimiza...
arXiv:2606. 00570v1 Announce Type: cross Abstract: Parameter-based knowledge editing updates the internal knowledge of large language models (LLMs) via localized weight modifications and has attracted significant attention.
DynEdit is a dynamic framework for editing long-sequence knowledge in large language models. It uses token-level entropy to guide non-uniform sliding windows, focusing on high-entropy, information-dense regions that are harder to edit. Experiments on three benchmarks and two backbone models show DynEdit outperforms baselines, achieving up to +14.0 BLEU points on challenging paraphrase queries and near-saturated semantic scores across diverse knowledge domains.
arXiv:2606. 10554v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly deployed in real-world applications that require access to up-to-date knowledge.
The paper introduces FOVEATED, a plug‑and‑play framework that improves atomic‑fact recall in unstructured knowledge editing (UKE) for large language models. By randomly shifting Rotary Position Embedding (RoPE) positions during editing, FOVEATED creates focused views of each sentence, counteracting the context‑reliance problem where edited LLMs reproduce passages but fail to recall individual facts. Experiments show consistent gains across five editors, two LLM backbones, and three benchmarks.
The paper introduces GLIME, a lifelong model editing framework that integrates knowledge editing with preference optimization to handle continual updates in large language models. GLIME employs replay-based editing and a gradient constraint to prevent overfitting to target prompts and preserve previously edited knowledge. Experiments demonstrate that GLIME enhances knowledge generalization while maintaining editing performance and overall model capabilities.
arXiv:2511. 20892v4 Announce Type: replace Abstract: Large language models (LLMs) often produce incorrect or outdated content after being employed.
arXiv:2607. 08646v1 Announce Type: cross Abstract: As available training data approaches its physical limit, gains from Scaling Laws have begun to diminish.
The paper introduces ALOE, a method for knowledge editing that learns semantic addresses from paraphrases and hard negatives, aligns them with autoregressive hidden states, and embeds a gated low‑rank operator within a single MLP layer. This design allows the edited model to run in one forward pass without external retrievers or routers. Experiments on CounterFact, ZSRE, and KnowEdit show high efficacy (0.955–0.999) and locality (0.981–1.000) across 7–8B model families, with analyses indicating effective separation of edits and suppression of out‑of‑scope activation.
The paper introduces GEdit, a geometry-aware framework for editing large language models that leverages a Low-Rank Plus Diagonal factor model to capture the low-rank structure of Transformer FFN hidden representations. GEdit applies low‑rank regularization to protect core knowledge and adaptive regularization to adjust constraints based on the difficulty of each edit, allowing harder edits to succeed while preserving easier ones. Experiments on GPT2‑XL, GPT‑J, and LLaMA‑3‑8B with up to 10,000 continuous updates demonstrate that GEdit outperforms state‑of‑the‑art baselines in both editing success and knowledge preservation.
arXiv:2511. 05852v4 Announce Type: replace-cross Abstract: Knowledge editing (KE) offers a lightweight alternative to retraining for updating large language models (LLMs).