Cost-Effective Hallucination Detection for LLMs
<|reference_start|>Cost-Effective Hallucination Detection for LLMs: Large language models (LLMs) can be prone to hallucinations - generating unreliable outputs that are unfaithful to their inputs, external facts or internally inconsistent. In this work, we address several challenges for post-hoc hallucination detection in production settings. Our pipeline for hallucination detection entails: first, producing a confidence score representing the likelihood that a generated answer is a hallucination; second, calibrating the score conditional on attributes of the inputs and candidate response; finally, performing detection by thresholding the calibrated score. We benchmark a variety of state-of-the-art scoring methods on different datasets, encompassing question answering, fact checking, and summarization tasks. We employ diverse LLMs to ensure a comprehensive assessment of performance. We show that calibrating individual scoring methods is critical for ensuring risk-aware downstream decision making. Based on findings that no individual score performs best in all situations, we propose a multi-scoring framework, which combines different scores and achieves top performance across all datasets. We further introduce cost-effective multi-scoring, which can match or even outperform more expensive detection methods, while significantly reducing computational overhead.<|reference_end|>
@article{valentin2024cost-effective,
    title={Cost-Effective Hallucination Detection for LLMs},
    author={Simon Valentin, Jinmiao Fu, Gianluca Detommaso, Shaoyuan Xu, Giovanni
  Zappella, Bryan Wang},
    journal={arXiv preprint arXiv:2407.21424},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.21424},
    primaryClass={cs.CL cs.AI cs.LG stat.ML}
}