Influences on LLM Calibration

Influences on LLM Calibration: A Study of Response Agreement, Loss Functions, and Prompt Styles

Authors: Yuxi Xia, Pedro Henrique Luz De Araujo, Klim Zaporojets, Benjamin Roth
Accepted at ACL 2025 (Association for Computational Linguistics)

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📁 Project Structure

Training the Model

• src/train.py — Main script for training models.

Data Preparation & Answer Generation

Generate Answers

• OpenAI-style API generation:
  src/generate_answers_openai_style.py

• HuggingFace models:
  src/generate_answers.py

Post-Processing

• src/post_processing.py — Clean and structure generated answers.

Evaluate Answer Correctness using LLM-as-a-Judge

• src/evaluate_prom_score.py

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📖 Acknowledgements

We build upon prior work in LLM calibration, particularly:

@inproceedings{ulmer-etal-2024-calibrating,
  title     = "Calibrating Large Language Models Using Their Generations Only",
  author    = "Ulmer, Dennis and Gubri, Martin and Lee, Hwaran and Yun, Sangdoo and Oh, Seong",
  booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
  month     = aug,
  year      = "2024",
  address   = "Bangkok, Thailand",
  publisher = "Association for Computational Linguistics",
  url       = "https://aclanthology.org/2024.acl-long.824/",
  doi       = "10.18653/v1/2024.acl-long.824",
  pages     = "15440--15459"
}

@inproceedings{tian-etal-2023-just,
  title     = "Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human Feedback",
  author    = "Tian, Katherine and Mitchell, Eric and Zhou, Allan and Sharma, Archit and Rafailov, Rafael and Yao, Huaxiu and Finn, Chelsea and Manning, Christopher",
  booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
  month     = dec,
  year      = "2023",
  address   = "Singapore",
  publisher = "Association for Computational Linguistics",
  url       = "https://aclanthology.org/2023.emnlp-main.330/",
  doi       = "10.18653/v1/2023.emnlp-main.330",
  pages     = "5433--5442"
}