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

Gabriel Y. Arteaga, Thomas B. Schön, Nicolas Pielawski
Proceedings of the 6th Northern Lights Deep Learning Conference (NLDL), PMLR 265:1-15, 2025.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v265-arteaga25a, title = {Hallucination Detection in {LLM}s: Fast and Memory-Efficient Finetuned Models}, author = {Arteaga, Gabriel Y. and Sch{\"o}n, Thomas B. and Pielawski, Nicolas}, booktitle = {Proceedings of the 6th Northern Lights Deep Learning Conference (NLDL)}, pages = {1--15}, year = {2025}, editor = {Lutchyn, Tetiana and Ramírez Rivera, Adín and Ricaud, Benjamin}, volume = {265}, series = {Proceedings of Machine Learning Research}, month = {07--09 Jan}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v265/main/assets/arteaga25a/arteaga25a.pdf}, url = {https://proceedings.mlr.press/v265/arteaga25a.html}, 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.} }
Endnote
%0 Conference Paper %T Hallucination Detection in LLMs: Fast and Memory-Efficient Finetuned Models %A Gabriel Y. Arteaga %A Thomas B. Schön %A Nicolas Pielawski %B Proceedings of the 6th Northern Lights Deep Learning Conference (NLDL) %C Proceedings of Machine Learning Research %D 2025 %E Tetiana Lutchyn %E Adín Ramírez Rivera %E Benjamin Ricaud %F pmlr-v265-arteaga25a %I PMLR %P 1--15 %U https://proceedings.mlr.press/v265/arteaga25a.html %V 265 %X 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.
APA
Arteaga, G.Y., Schön, T.B. & Pielawski, N.. (2025). Hallucination Detection in LLMs: Fast and Memory-Efficient Finetuned Models. Proceedings of the 6th Northern Lights Deep Learning Conference (NLDL), in Proceedings of Machine Learning Research 265:1-15 Available from https://proceedings.mlr.press/v265/arteaga25a.html.

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