chengpingan/PIP-KAG-7B

🤖 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.

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📚 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

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📊 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}, 
}