Hallucination Detection in LLMs: Fast and Memory-Efficient Fine-Tuned Models
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866929617710874624 |
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| author | Arteaga, Gabriel Y. Schön, Thomas B. Pielawski, Nicolas |
| author_facet | Arteaga, Gabriel Y. Schön, Thomas B. Pielawski, Nicolas |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_02976 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Hallucination Detection in LLMs: Fast and Memory-Efficient Fine-Tuned Models Arteaga, Gabriel Y. Schön, Thomas B. Pielawski, Nicolas Machine Learning Artificial Intelligence Computation and Language 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. |
| title | Hallucination Detection in LLMs: Fast and Memory-Efficient Fine-Tuned Models |
| topic | Machine Learning Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2409.02976 |