# Hallucination Detection in LLMs: Fast and Memory-Efficient Finetuned Models

**Authors:** Gabriel Y. Arteaga, Thomas B. Schön, Nicolas Pielawski  
**arXiv:** [2409.02976](https://arxiv.org/abs/2409.02976)  
**Date:** 2024-09-04  
**Summary page:** https://www.summarizepaper.com/fr/paper/2409.02976v1/

## 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.