Identifying Knowledge Editing Types in Large Language Models

• URL: http://arxiv.org/abs/2409.19663v3
• Date: Mon, 26 May 2025 00:38:49 GMT
• Title: Identifying Knowledge Editing Types in Large Language Models
• Authors: Xiaopeng Li, Shasha Li, Shangwen Wang, Shezheng Song, Bin Ji, Huijun Liu, Jun Ma, Jie Yu,
• Abstract summary: Knowledge editing has emerged as an efficient technique for updating the knowledge of large language models (LLMs)<n>There is a lack of effective measures to prevent the malicious misuse of this technique, which could lead to harmful edits in LLMs.<n>We introduce a new task, $textbfK$nowledge $textbfE$diting $textbfT$ype $textbfI$dentification (KETI), aimed at identifying different types of edits in LLMs.
• Score: 11.051687980330286
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Knowledge editing has emerged as an efficient technique for updating the knowledge of large language models (LLMs), attracting increasing attention in recent years. However, there is a lack of effective measures to prevent the malicious misuse of this technique, which could lead to harmful edits in LLMs. These malicious modifications could cause LLMs to generate toxic content, misleading users into inappropriate actions. In front of this risk, we introduce a new task, $\textbf{K}$nowledge $\textbf{E}$diting $\textbf{T}$ype $\textbf{I}$dentification (KETI), aimed at identifying different types of edits in LLMs, thereby providing timely alerts to users when encountering illicit edits. As part of this task, we propose KETIBench, which includes five types of harmful edits covering the most popular toxic types, as well as one benign factual edit. We develop five classical classification models and three BERT-based models as baseline identifiers for both open-source and closed-source LLMs. Our experimental results, across 92 trials involving four models and three knowledge editing methods, demonstrate that all eight baseline identifiers achieve decent identification performance, highlighting the feasibility of identifying malicious edits in LLMs. Additional analyses reveal that the performance of the identifiers is independent of the reliability of the knowledge editing methods and exhibits cross-domain generalization, enabling the identification of edits from unknown sources. All data and code are available in https://github.com/xpq-tech/KETI.

Related papers

• Latent Knowledge Scalpel: Precise and Massive Knowledge Editing for Large Language Models [3.834827405473377]
  Large Language Models (LLMs) often retain inaccurate or outdated information from pre-training, leading to incorrect predictions or biased outputs during inference.<n>We introduce the Latent Knowledge Scalpel (LKS), an LLM editor that manipulates the latent knowledge of specific entities via a lightweight hypernetwork to enable precise and large-scale editing.<n> Experiments conducted on Llama-2 and Mistral show even with the number of simultaneous edits reaching 10,000, LKS effectively performs knowledge editing while preserving the general abilities of the edited LLMs.
  arXiv  Detail & Related papers  (2025-08-01T03:51:43Z)
• Adaptive Detoxification: Safeguarding General Capabilities of LLMs through Toxicity-Aware Knowledge Editing [49.85884082568318]
  ToxEdit is a toxicity-aware knowledge editing approach.<n>It dynamically detects toxic activation patterns during forward propagation.<n>It then routes computations through adaptive inter-layer pathways to mitigate toxicity effectively.
  arXiv  Detail & Related papers  (2025-05-28T12:37:06Z)
• Uncovering Overfitting in Large Language Model Editing [35.55260822503773]
  We identify and investigate the phenomenon of Editing Overfit, where edited models assign disproportionately high probabilities to the edit target. We propose a new plug-and-play strategy called Learn to Inference (LTI), which introduce a Multi-stage Inference Constraint module to guide the edited models in recalling new knowledge.
  arXiv  Detail & Related papers  (2024-10-10T11:09:00Z)
• AlphaEdit: Null-Space Constrained Knowledge Editing for Language Models [65.93240009586351]
  Large language models (LLMs) often exhibit hallucinations due to incorrect or outdated knowledge. We introduce AlphaEdit, a novel solution that projects perturbation onto the null space of the preserved knowledge before applying it to the parameters. We theoretically prove that this projection ensures the output of post-edited LLMs remains unchanged when queried about the preserved knowledge.
  arXiv  Detail & Related papers  (2024-10-03T10:06:27Z)
• ELDER: Enhancing Lifelong Model Editing with Mixture-of-LoRA [55.697627106315004]
  Large language models (LLMs) require model editing to efficiently update specific knowledge within them and avoid factual errors.<n>Previous approaches manage sequential edits by freezing original parameters and discretely allocating new parameters for each knowledge update.<n>We propose ELDER, a novel approach to create a continuous association between data and adapters.
  arXiv  Detail & Related papers  (2024-08-19T02:27:00Z)
• Can Editing LLMs Inject Harm? [122.83469484328465]
  We propose to reformulate knowledge editing as a new type of safety threat for Large Language Models. For the risk of misinformation injection, we first categorize it into commonsense misinformation injection and long-tail misinformation injection. For the risk of bias injection, we discover that not only can biased sentences be injected into LLMs with high effectiveness, but also one single biased sentence injection can cause a bias increase.
  arXiv  Detail & Related papers  (2024-07-29T17:58:06Z)
• How Well Can Knowledge Edit Methods Edit Perplexing Knowledge? [18.022428746019582]
  This study investigates the capability of knowledge editing methods to incorporate new knowledge with varying degrees of "perplexingness" We find significant negative correlations between the "perplexingness" of the new knowledge and the edit efficacy across all 12 scenarios. Further exploration into the influence of knowledge hierarchy on editing outcomes indicates that knowledge positioned at higher hierarchical levels is more challenging to modify in some scenarios.
  arXiv  Detail & Related papers  (2024-06-25T03:41:02Z)
• Detecting Edited Knowledge in Language Models [5.260519479124422]
  Knowledge editing methods (KEs) can update language models' obsolete or inaccurate knowledge learned from pre-training. Knowing whether a generated output is based on edited knowledge or first-hand knowledge from pre-training can increase users' trust in generative models. We propose a novel task: detecting edited knowledge in language models.
  arXiv  Detail & Related papers  (2024-05-04T22:02:24Z)
• Editing Conceptual Knowledge for Large Language Models [65.38231526537476]
  This paper pioneers the investigation of editing conceptual knowledge for Large Language Models (LLMs) We construct a novel benchmark dataset ConceptEdit and establish a suite of new metrics for evaluation. experimental results reveal that, although existing editing methods can efficiently modify concept-level definition to some extent, they also have the potential to distort the related instantial knowledge.
  arXiv  Detail & Related papers  (2024-03-10T16:57:10Z)
• Knowledge Graph Enhanced Large Language Model Editing [37.6721061644483]
  Large language models (LLMs) are pivotal in advancing natural language processing (NLP) tasks. Existing editing methods struggle to track and incorporate changes in knowledge associated with edits. We propose a novel model editing method that leverages knowledge graphs for enhancing LLM editing, namely GLAME.
  arXiv  Detail & Related papers  (2024-02-21T07:52:26Z)
• Learning to Edit: Aligning LLMs with Knowledge Editing [101.96620267293731]
  We propose a Learning to Edit (LTE) framework, focusing on teaching large language models to apply updated knowledge into input questions. LTE features a two-phase process: (i) the Alignment Phase, which fine-tunes LLMs on a meticulously curated parallel dataset to make reliable, in-scope edits. We demonstrate LTE's superiority in knowledge editing performance, robustness in both batch and sequential editing, minimal interference on general tasks, and rapid editing speeds.
  arXiv  Detail & Related papers  (2024-02-19T07:45:17Z)
• Unveiling the Pitfalls of Knowledge Editing for Large Language Models [41.83423510576848]
  It is still unclear whether knowledge editing might introduce side effects that pose potential risks or not. This paper pioneers the investigation into the potential pitfalls associated with knowledge editing for Large Language Models. Experimental results vividly demonstrate that knowledge editing might inadvertently cast a shadow of unintended consequences.
  arXiv  Detail & Related papers  (2023-10-03T15:10:46Z)
• Cross-Lingual Knowledge Editing in Large Language Models [73.12622532088564]
  Knowledge editing has been shown to adapt large language models to new knowledge without retraining from scratch. It is still unknown the effect of source language editing on a different target language. We first collect a large-scale cross-lingual synthetic dataset by translating ZsRE from English to Chinese.
  arXiv  Detail & Related papers  (2023-09-16T11:07:52Z)
• Editing Large Language Models: Problems, Methods, and Opportunities [51.903537096207]
  This paper embarks on a deep exploration of the problems, methods, and opportunities related to model editing for LLMs. We provide an exhaustive overview of the task definition and challenges associated with model editing, along with an in-depth empirical analysis of the most progressive methods currently at our disposal. Our objective is to provide valuable insights into the effectiveness and feasibility of each editing technique, thereby assisting the community in making informed decisions on the selection of the most appropriate method for a specific task or context.
  arXiv  Detail & Related papers  (2023-05-22T16:00:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.