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---
license: cc-by-nc-sa-4.0
---
<div align="center">
**Editing Conceptual Knowledge for Large Language Models**
---
<p align="center">
<a href="#-conceptual-knowledge-editing">Overview</a> •
<a href="#-usage">How To Use</a> •
<a href="#-citation">Citation</a> •
<a href="https://arxiv.org/abs/2403.06259">Paper</a> •
<a href="https://zjunlp.github.io/project/ConceptEdit">Website</a>
</p>
</div>
## 💡 Conceptual Knowledge Editing
<div align=center>
<img src="./flow1.gif" width="70%" height="70%" />
</div>
### Task Definition
**Concept** is a generalization of the world in the process of cognition, which represents the shared features and essential characteristics of a class of entities.
Therefore, the endeavor of concept editing aims to modify the definition of concepts, thereby altering the behavior of LLMs when processing these concepts.
### Evaluation
To analyze conceptual knowledge modification, we adopt the metrics for factual editing (the target is the concept $C$ rather than factual instance $t$).
- `Reliability`: the success rate of editing with a given editing description
- `Generalization`: the success rate of editing **within** the editing scope
- `Locality`: whether the model's output changes after editing for unrelated inputs
Concept Specific Evaluation Metrics
- `Instance Change`: capturing the intricacies of these instance-level changes
- `Concept Consistency`: the semantic similarity of generated concept definition
## 🌟 Usage
### 🎍 Current Implementation
As the main Table of our paper, four editing methods are supported for conceptual knowledge editing.
| **Method** | GPT-2 | GPT-J | LlaMA2-13B-Chat | Mistral-7B-v0.1
| :--------------: | :--------------: | :--------------: | :--------------: | :--------------: |
| FT | ✅ | ✅ | ✅ | ✅ |
| ROME | ✅ | ✅ |✅ | ✅ |
| MEMIT | ✅ | ✅ | ✅| ✅ |
| PROMPT | ✅ | ✅ | ✅ | ✅ |
### 💻 Run
You can follow [EasyEdit](https://github.com/zjunlp/EasyEdit/edit/main/examples/ConceptEdit.md) to run the experiments.
## 📖 Citation
Please cite our paper if you use **ConceptEdit** in your work.
```bibtex
@misc{wang2024editing,
title={Editing Conceptual Knowledge for Large Language Models},
author={Xiaohan Wang and Shengyu Mao and Ningyu Zhang and Shumin Deng and Yunzhi Yao and Yue Shen and Lei Liang and Jinjie Gu and Huajun Chen},
year={2024},
eprint={2403.06259},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
## 🎉 Acknowledgement
We would like to express our sincere gratitude to [DBpedia](https://www.dbpedia.org/resources/ontology/)，[Wikidata](https://www.wikidata.org/wiki/Wikidata:Introduction)，[OntoProbe-PLMs](https://github.com/vickywu1022/OntoProbe-PLMs) and [ROME](https://github.com/kmeng01/rome).
Their contributions are invaluable to the advancement of our work.
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