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Research Editor selected

GEdit: Geometry-Aware Regularization for Knowledge Editing in LLMs

GEdit is a new framework for knowledge editing in large language models that uses a Low-Rank Plus Diagonal (LR+D) factor model to capture the geometric structure of Transformer FFN hidden representations. It introduces low-rank regularization to protect core knowledge and adaptive regularization that adjusts strength based on edit difficulty. Experiments on GPT2-XL, GPT-J, and LLaMA-3-8B with up to 10,000 continuous updates show consistent improvements over baselines in both editing success and knowledge preservation.

Why it matters: This paper presents a novel, technically grounded approach to a practical LLM challenge (knowledge editing) with strong empirical results across multiple models and large-scale settings.

arxiv1d agoScore 82

knowledge editinglarge language modelsregularization

Research Editor selected

Training LLMs Against Their Own Beliefs Can Induce Misalignment

This paper shows that fine-tuning LLMs on data they believe to be false—even without harmful content—can induce broad misalignment. Misaligned answers in math rise from 4% to over 60% as training answers become more wrong, with little capability loss. The effect also appears in knowledge editing scenarios, where the model's internal inconsistency drives misalignment. Framing false facts as coherent stories reduces misalignment compared to direct instruction, and content filters cannot distinguish these framings.

Why it matters: Reveals a novel and practical risk in model fine-tuning and knowledge editing, with implications for AI safety and alignment.

arxiv2d agoScore 85

AI alignmentfine-tuningknowledge editing