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.
By Ding Wu, Ye Zhang, Haoyu Wang, Tianci Liu
arXiv:2602.20207v4 Announce Type: replace-cross
Abstract: Knowledge editing in Large Language Models (LLMs) aims to update the model's prediction for a specific query to a desired target while preser...
By Shrestha Datta, Hongfu Liu, Anshuman Chhabra
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.
By Zeyan Li, Hu Xu, Jianfeng Xu
arXiv:2608. 11660v1 Announce Type: cross Abstract: Large language models (LLMs) achieve remarkable performance across natural language tasks, yet they are trained on static corpora and their knowledge quickly becomes outdated in a fast-changing world.
By Tianci Liu, Zihan Dong, Tianchun Li, Yi-Chung Chen, Qiming Cao, Xingchen Wang, Shiyang Wang, Zichen Miao, Linjun Zhang, Haoyu Wang, Jing Gao
arXiv:2607. 08646v1 Announce Type: cross Abstract: As available training data approaches its physical limit, gains from Scaling Laws have begun to diminish.
By Xinlong Zhao, Dongsheng Liu, Hengyu Zhao, Zixuan Fu, Zheng Wang, Jie Cai, Jie Zhou, Qiang Ma, Xuanhe Zhou, Xu Han, Yudong Wang, Zhiyuan Liu
The paper introduces PARALLELEVENTS, a benchmark that simulates realistic future event trajectories to evaluate knowledge updates in large language models without contaminating data or conflicting with existing knowledge. It also presents SYNAPSE, a training framework that uses model-generated synthetic data for instruction tuning, enabling scalable integration of new knowledge. SYNAPSE achieves a 14.23‑point improvement in QA accuracy over previous methods, demonstrating the effectiveness of simulation‑based synthetic training for coherent knowledge insertion.
By Jonathan Zheng, Zirui Shao, Alan Ritter, Wei Xu