← Back to all news
arXiv AI By Dahyun Jung, Suhyune Son, Heuiseok Lim

Generalizable Lifelong Model Editing via Preference Optimization

Read the original on arXiv AI →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

Related stories

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.

llmsbenchmarks
More like this →
arXiv AI
Jun 19

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

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.

By Masih Eskandar, Miquel Sirera Perell\'o, Stratis Ioannidis, Jennifer Dy
llms
More like this →
arXiv Computation and Language
Sep 2

InComeS: Integrating Compression and Selection Mechanisms into LLMs for Efficient Model Editing

InComeS is a framework that improves large language models (LLMs) for model editing by compressing editing contexts into a key‑value cache associated with a special gist token. It adds cross‑attention modules to dynamically select the most relevant information from these gist pools, allowing efficient handling of multiple edits beyond the LLM’s context window. Experiments on diverse editing benchmarks show that InComeS enhances both effectiveness and efficiency compared to existing methods.

By Shuaiyi Li, Zhisong Zhang, Yang Deng, Chenlong Deng, Tianqing Fang, Hongming Zhang, Haitao Mi, Dong Yu, Wai Lam
llmsbenchmarks
More like this →