arXiv AI By Masih Eskandar, Miquel Sirera Perell\'o, Stratis Ioannidis, Jennifer Dy

LOKI: Memory-Free Null-Space Constrained Lifelong Knowledge Editing

Read the original on arXiv AI →

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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI
Aug 11

Beyond Static Models: An Evolving Framework for Continual Learning in Large Language Models across Training Stages

arXiv:2603. 12658v2 Announce Type: replace-cross Abstract: Continual learning (CL) has emerged as a pivotal paradigm to enable large language models (LLMs) to dynamically adapt to evolving knowledge and sequential tasks while mitigating catastrophic forgetting, a critical limitation of the static pre-training paradigm inherent to modern LLMs.

By Hongyang Chen, Zhongwu Sun, Hongfei Ye, Kunchi Li, Xuemin Lin
Hugging Face Trending Papers
Jul 29

ForgetBench: Benchmarking Forgetting Dynamics of Long-Term Parametric Memory in Language Models

Large language models (LLMs) have demonstrated strong capabilities in knowledge acquisition and reasoning, yet their ability to retain previously acquired knowledge under repeated updates remains insufficiently understood. Existing evaluation paradigms primarily focus on single-step reasoning or static knowledge editing, which fail to capture the temporal dynamics of knowledge retention and degradation during continual model modification.