Benchmarking Large Language Model Uncertainty for Prompt Optimization
<|reference_start|>Benchmarking Large Language Model Uncertainty for Prompt Optimization: Prompt optimization algorithms for Large Language Models (LLMs) excel in multi-step reasoning but still lack effective uncertainty estimation. This paper introduces a benchmark dataset to evaluate uncertainty metrics, focusing on Answer, Correctness, Aleatoric, and Epistemic Uncertainty. Through analysis of models like GPT-3.5-Turbo and Meta-Llama-3.1-8B-Instruct, we show that current metrics align more with Answer Uncertainty, which reflects output confidence and diversity, rather than Correctness Uncertainty, highlighting the need for improved metrics that are optimization-objective-aware to better guide prompt optimization. Our code and dataset are available at https://github.com/0Frett/PO-Uncertainty-Benchmarking.<|reference_end|>
@article{guo2024benchmarking,
    title={Benchmarking Large Language Model Uncertainty for Prompt Optimization},
    author={Pei-Fu Guo, Yun-Da Tsai, Shou-De Lin},
    journal={arXiv preprint arXiv:2409.10044},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.10044},
    primaryClass={cs.LG cs.CL}
}