An Easy-to-use Knowledge Editing Framework for Large Language Models.
###  [](https://opensource.org/licenses/MIT)  
**EasyEdit** is a Python package built upon [PyTorch](https://pytorch.org/) for edit Large Language Models (LLM) like `GPT-J`, `Llama`, `GPT-NEO`, `GPT2`, `T5`(support models from **1B** to **65B**), altering the behavior of LLMs efficiently without negatively impacting performance across other inputs.
If you find EasyEdit useful, please kindly cite this [paper](https://arxiv.org/abs/2305.13172).
```tex
@misc{yao2023editing,
title={Editing Large Language Models: Problems, Methods, and Opportunities},
author={Yunzhi Yao and Peng Wang and Bozhong Tian and Siyuan Cheng and Zhoubo Li and Shumin Deng and Huajun Chen and Ningyu Zhang},
year={2023},
eprint={2305.13172},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
***
### Model Editing
Deployed models may still make unpredictable errors. For example, Large Language Models (LLMs) notoriously *hallucinate*, *perpetuate bias*, and *factually decay*, so we should be able to adjust specific behaviors of pre-trained models.
**Model editing** aims to adjust an initial base model's ($$f\_{\theta}$$) behavior on the particular edit descriptor $$\[x\_e, y\_e]$$ efficiently, such as(The president of USA: Donald Trump -> Joe Biden):
* $$x\_e$$: Who is the president of the US
* $$y\_e$$: Joe Biden
without influencing the model behavior on unrelated samples. The ultimate goal is to create an edited model ($$f\_{\theta}'$$).
### Colab Tutorial
You can go colab directly
* [colab link](https://colab.research.google.com/drive/1zcj8YgeqttwkpfoHXz9O9_rWxFFufXSO)
