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README.md

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 <!-- Provide a quick summary of what the model is/does. -->
 
+# 🤖 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](https://arxiv.org/pdf/2502.15543)**.  
 
-## Model Details
+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.
 
-### Model Description
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-<!-- Provide a longer summary of what this model is. -->
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-- **Developed by:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** English
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** LLaMA3-8B-Instruct.
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-### Model Sources [optional]
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-## Uses
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-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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-### Direct Use
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-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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-### Downstream Use [optional]
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-### Out-of-Scope Use
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-## Bias, Risks, and Limitations
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-### Recommendations
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-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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-## How to Get Started with the Model
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-Use the code below to get started with the model.
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-## Training Details
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-### Training Data
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-### Training Procedure
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-#### Preprocessing [optional]
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-#### Training Hyperparameters
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-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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-#### Speeds, Sizes, Times [optional]
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-## Evaluation
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-<!-- This section describes the evaluation protocols and provides the results. -->
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-### Testing Data, Factors & Metrics
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-#### Testing Data
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-#### Factors
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-#### Metrics
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-### Results
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-#### Summary
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-## Model Examination [optional]
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-<!-- Relevant interpretability work for the model goes here -->
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-## Environmental Impact
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-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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-- **Hardware Type:** [More Information Needed]
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-## Technical Specifications [optional]
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-### Model Architecture and Objective
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-### Compute Infrastructure
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-#### Hardware
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-#### Software
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-## Citation [optional]
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-**BibTeX:**
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-## Glossary [optional]
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-## More Information [optional]
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-## Model Card Authors [optional]
+---
 
-[More Information Needed]
+## 📚 **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**](https://arxiv.org/abs/2502.15543)
 
-## Model Card Contact
+---
 
-[More Information Needed]
-### Framework versions
+# 📊 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](https://github.com/OpenBMB/PIP-KAG).
+
+# 📁 Model Details
+Model Name: PIP-KAG-7B
+Architecture: LLaMA3-8B-Instruct with Parametric Pruning
+Training Data: [CoConflictQA](https://huggingface.co/datasets/chengpingan/PIP-KAG) 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}, 
+}
+
+```
 
 - PEFT 0.14.0 -->