Efficiently editing the knowledge of large language models (LLMs) is crucial for real-world adaptation and error correction, yet remains a fundamental challenge. Existing approaches, primarily based on the Locate-Then-Edit paradigm, have achieved promising results by first identifying key parameters and then updating them; however, they still suffer from catastrophic over-editing, where sequential updates degrade both preserved and edited knowledge, especially as batch sizes grow. To address this, we propose Important Weight Constrained Knowledge Editing (IWCKEdit), a simple yet effective framework that introduces weight protection into the model editing process. IWCKEdit comprises two key modules: a front-end Protected Weight Identification Module that leverages Fisher information to detect weights essential for knowledge retention and effective editing, and a back-end Constrained Weight Updating Module that safeguards these critical weights during targeted updates for knowledge editing. Extensive experiments on large-scale models including GPT2-XL, Llama3-8B, and Qwen2.5-7B demonstrate that IWCKEdit achieves superior knowledge editing accuracy while robustly preserving both newly introduced and existing knowledge, substantially advancing the state of the art in LLM editing.

Zifeng Zhu, Mengzhao Jia, Zhihan Zhang, Lang Li, Meng Jiang. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

As malicious actors employ increasingly advanced and widespread bots to disseminate misinformation and manipulate public opinion, the detection of Twitter bots has become a crucial task. Though graph-based Twitter bot detection methods achieve state-of-the-art performance, we find that their inference depends on the neighbor users multi-hop away from the targets, and fetching neighbors is time-consuming and may introduce sampling bias. At the same time, our experiments reveal that after finetuning on Twitter bot detection task, pretrained language models achieve competitive performance while do not require a graph structure during deployment. Inspired by this finding, we propose a novel bot detection framework LMBot that distills the graph knowledge into language models (LMs) for graph-less deployment in Twitter bot detection to combat data dependency challenge. Moreover, LMBot is compatible with graph-based and graph-less datasets. Specifically, we first represent each user as a textual sequence and feed them into the LM for domain adaptation. For graph-based datasets, the output of LM serves as input features for the GNN, enabling LMBot to optimize for bot detection and distill knowledge back to the LM in an iterative, mutually enhancing process. Armed with the LM, we can perform graph-less inference with graph knowledge, which resolves the graph data dependency and sampling bias issues. For datasets without graph structure, we simply replace the GNN with an MLP, which also shows strong performance. Our experiments demonstrate that LMBot achieves state-of-the-art performance on four Twitter bot detection benchmarks. Extensive studies also show that LMBot is more robust, versatile, and efficient compared to existing graph-based Twitter bot detection methods.