Calibrated Large Language Models for Binary Question Answering
<|reference_start|>Calibrated Large Language Models for Binary Question Answering: Quantifying the uncertainty of predictions made by large language models (LLMs) in binary text classification tasks remains a challenge. Calibration, in the context of LLMs, refers to the alignment between the model's predicted probabilities and the actual correctness of its predictions. A well-calibrated model should produce probabilities that accurately reflect the likelihood of its predictions being correct. We propose a novel approach that utilizes the inductive Venn--Abers predictor (IVAP) to calibrate the probabilities associated with the output tokens corresponding to the binary labels. Our experiments on the BoolQ dataset using the Llama 2 model demonstrate that IVAP consistently outperforms the commonly used temperature scaling method for various label token choices, achieving well-calibrated probabilities while maintaining high predictive quality. Our findings contribute to the understanding of calibration techniques for LLMs and provide a practical solution for obtaining reliable uncertainty estimates in binary question answering tasks, enhancing the interpretability and trustworthiness of LLM predictions.<|reference_end|>
@article{giovannotti2024calibrated,
    title={Calibrated Large Language Models for Binary Question Answering},
    author={Patrizio Giovannotti and Alexander Gammerman},
    journal={arXiv preprint arXiv:2407.01122},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.01122},
    primaryClass={cs.CL cs.LG}
}