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.
@article{arxiv.2409.02976,
title = {Hallucination Detection in LLMs: Fast and Memory-Efficient Fine-Tuned Models},
author = {Gabriel Y. Arteaga and Thomas B. Schön and Nicolas Pielawski},
journal= {arXiv preprint arXiv:2409.02976},
year = {2024}
}