An Information-Theoretic Framework for Robust Large Language Model Editing

2025-12-18

AI Summary

To address key bottlenecks in large language model (LLM) editing—namely, knowledge obsolescence, poor generalization across edits, and severe side effects—this work pioneers the application of information bottleneck theory to model editing, proposing a “knowledge compression–isolation–precise update” paradigm. Our method achieves semantic disentanglement via compact latent-space modeling, enables low-interference, interpretable knowledge correction through gradient-guided local parameter updates, and ensures broad compatibility across architectures via a unified editing interface—all without full model retraining. Evaluated on multiple LLMs and standard editing benchmarks, it achieves state-of-the-art performance: significantly improving cross-domain edit generalization and specificity, enabling robust open-domain knowledge updates, and providing a novel, safe, and controllable framework for LLM knowledge maintenance.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Computer Vision: Large Vision Models

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

Abstract

Large Language Models (LLMs) have become indispensable tools in science, technology, and society, enabling transformative advances across diverse fields. However, errors or outdated information within these models can undermine their accuracy and restrict their safe deployment. Developing efficient strategies for updating model knowledge without the expense and disruption of full retraining remains a critical challenge. Current model editing techniques frequently struggle to generalize corrections beyond narrow domains, leading to unintended consequences and limiting their practical impact. Here, we introduce a novel framework for editing LLMs, grounded in information bottleneck theory. This approach precisely compresses and isolates the essential information required for generalizable knowledge correction while minimizing disruption to unrelated model behaviors. Building upon this foundation, we present the Information Bottleneck Knowledge Editor (IBKE), which leverages compact latent representations to guide gradient-based updates, enabling robust and broadly applicable model editing. We validate IBKE's effectiveness across multiple LLM architectures and standard benchmark tasks, demonstrating state-of-the-art accuracy and improved generality and specificity of edits. These findings establish a theoretically principled and practical paradigm for open-domain knowledge editing, advancing the utility and trustworthiness of LLMs in real-world applications.

Problem

• Develops efficient strategies to update LLM knowledge without full retraining
• Addresses poor generalization of corrections beyond narrow domains in editing
• Minimizes disruption to unrelated model behaviors during knowledge correction

Innovation

• Information bottleneck theory for generalizable knowledge correction
• Compact latent representations guide gradient-based updates
• Minimizes disruption to unrelated model behaviors