Restoring Calibration for Aligned Large Language Models

ICML 2025 | Paper: Restoring Calibration for Aligned Large Language Models: A Calibration-Aware Fine-Tuning Approach

📄 Paper on OpenReview

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🧰 Code Structure

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├── scripts/             # Training & evaluation scripts
├── train/               # LoRA + Fine-tuning code
├── inference/           # Inference Code
├── data/                # Dataset processing
├── utils/               # Util functions
├── plots/               # Calibration visualizations
└── README.md            # This file

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🚀 Getting Started

1. Environment Setup

conda create -n llm-calibration python=3.10
conda activate llm-calibration
pip install -r requirements.txt

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📌 Citation

@misc{xiao2025restoringcalibrationalignedlarge,
      title={Restoring Calibration for Aligned Large Language Models: A Calibration-Aware Fine-Tuning Approach}, 
      author={Jiancong Xiao and Bojian Hou and Zhanliang Wang and Ruochen Jin and Qi Long and Weijie J. Su and Li Shen},
      year={2025},
      eprint={2505.01997},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2505.01997}, 
}

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🤝 Acknowledgements

This project is developed by researchers at the University of Pennsylvania. We thank the open-source community and prior foundational work on LLM calibration, DPO, and RLHF.