---
license: cc-by-nc-sa-4.0
task_categories:
- text-generation
tags:
- knowledge-editing
- llm
- conceptual-knowledge
---

<div align="center">

**Editing Conceptual Knowledge for Large Language Models**

---

<p align="center">
  <a href="https://huggingface.co/papers/2403.06259">Dataset Paper (ConceptEdit)</a> •
  <a href="https://zjunlp.github.io/project/ConceptEdit">Dataset Website</a> •
  <a href="https://github.com/zjunlp/EasyEdit">EasyEdit GitHub</a> •
  <a href="https://huggingface.co/papers/2504.15133">Framework Paper (EasyEdit2)</a> •
  <a href="https://zjunlp.github.io/project/EasyEdit2/">Framework Website (EasyEdit2)</a> 
</p>
</div>

This repository contains the `ConceptEdit` dataset, which facilitates the evaluation of Large Language Models (LLMs) in open-domain natural language-driven molecule generation tasks. It is part of the `EasyEdit` framework, designed for plug-and-play adjustability in controlling LLM behaviors.

## 💡 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

## 🚀 Sample Usage

You can load the ConceptEdit dataset using the `EasyEdit` framework as follows:

```python
from easyeditor import ConceptEditDataset

# For example, to load the ConceptEdit dataset
# Assuming 'data/concept_data.json' is the path to your dataset file
concept_config = ConceptEditDataset.get_hparams()
concept_train_ds = ConceptEditDataset('./data/concept_data.json', config=concept_config)
concept_eval_ds = ConceptEditDataset('./data/concept_data.json', config=concept_config)

print(f"Loaded {len(concept_train_ds)} training samples.")
print(f"Loaded {len(concept_eval_ds)} evaluation samples.")
# You can inspect a sample:
# print(concept_train_ds[0])
```

## 📖 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.