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arXiv · 2409.02976

Hallucination Detection in LLMs: Fast and Memory-Efficient Fine-Tuned Models

Gabriel Y. Arteaga·Thomas B. Schön·Nicolas Pielawski
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Abstract

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.

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Gabriel Y. Arteaga, Thomas B. Schön, Nicolas Pielawski. 2024-09-04. Hallucination Detection in LLMs: Fast and Memory-Efficient Fine-Tuned Models. https://arxiv.org/abs/2409.02976

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