Hallucination Detection in LLMs: Fast and Memory-Efficient Finetuned Models
<|reference_start|>Hallucination Detection in LLMs: Fast and Memory-Efficient Finetuned Models: Uncertainty estimation is a necessary component when implementing AI in high-risk settings, such as autonomous cars, medicine, or insurances. Large Language Models (LLMs) have seen a surge in popularity in recent years, but they are subject to hallucinations, which may cause serious harm in high-risk settings. Despite their success, LLMs are expensive to train and run: they need a large amount of computations and memory, preventing the use of ensembling methods in practice. In this work, we present a novel method that allows for fast and memory-friendly training of LLM ensembles. We show that the resulting ensembles can detect hallucinations and are a viable approach in practice as only one GPU is needed for training and inference.<|reference_end|>
@article{arteaga2024hallucination,
    title={Hallucination Detection in LLMs: Fast and Memory-Efficient Finetuned
  Models},
    author={Gabriel Y. Arteaga, Thomas B. Sch"on and Nicolas Pielawski},
    journal={arXiv preprint arXiv:2409.02976},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.02976},
    primaryClass={cs.LG cs.AI cs.CL}
}