EasyEdit

Why we included this project

EasyEdit targets anyone who needs to change what a large language model knows without retraining it from scratch. It collects a broad set of model-editing methods, from parameter-update approaches like ROME and MEMIT to newer representation-steering techniques, all behind a single BaseEditor interface, so you can run or compare them on GPT-J, Llama, and T5-class models without reimplementing each algorithm. The framework also ships evaluation utilities and a companion benchmark to check that an edit took effect without damaging other outputs. It stays a research-oriented package, with a growing collection of algorithms and beginner documentation, so it suits both practitioners prototyping knowledge-correction workflows and researchers reproducing or extending published editing methods.