base_model: Models/llama3-8b-instruct
library_name: peft
language:
- en
Model Card for Model ID
🤖 PIP-KAG: Mitigating Knowledge Conflicts in Knowledge-Augmented Generation via Parametric Pruning
This is the official model for PIP-KAG: Mitigating Knowledge Conflicts in Knowledge-Augmented Generation via Parametric Pruning.
The PIP-KAG model is designed to address knowledge conflicts in knowledge-augmented generation tasks by leveraging a parametric pruning strategy, improving the contextual faithfulness of language models during knowledge-intensive generation.
📚 Paper
For a detailed explanation of the methodology and experiments, please refer to our paper:
PIP-KAG: Mitigating Knowledge Conflicts in Knowledge-Augmented Generation via Parametric Pruning
📊 Reproduce the Results
To reproduce the experiments and benchmarks from the paper, follow the instructions provided in the official GitHub repository: 👉 GitHub: OpenBMB/PIP-KAG.
📁 Model Details
- Model Name: PIP-KAG-7B
- Architecture: LLaMA3-8B-Instruct with Parametric Pruning
- Training Data: CoConflictQA Dataset
- Pretrained Tasks: Knowledge-Augmented Generation, Contextual Faithfulness Evaluation
🔖 Citation
If you use PIP-KAG in your work, please consider citing our paper:
@misc{huang2025pipkagmitigatingknowledgeconflicts,
title={PIP-KAG: Mitigating Knowledge Conflicts in Knowledge-Augmented Generation via Parametric Pruning},
author={Pengcheng Huang and Zhenghao Liu and Yukun Yan and Xiaoyuan Yi and Hao Chen and Zhiyuan Liu and Maosong Sun and Tong Xiao and Ge Yu and Chenyan Xiong},
year={2025},
eprint={2502.15543},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2502.15543},
}