--- license: cc-by-nc-sa-4.0 ---
**Editing Conceptual Knowledge for Large Language Models** ---

OverviewHow To UseCitationPaperWebsite

## 💡 Conceptual Knowledge Editing
### 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.