Uncertainty quantification in fine-tuned LLMs using LoRA ensembles

Oleksandr Balabanov; Hampus Linander

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arXiv · 2402.12264

Uncertainty quantification in fine-tuned LLMs using LoRA ensembles

Oleksandr Balabanov·Hampus Linander

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Abstract

Fine-tuning large language models can improve task specific performance, although a general understanding of what the fine-tuned model has learned, forgotten and how to trust its predictions is still missing. We derive principled uncertainty quantification for fine-tuned LLMs with posterior approximations using computationally efficient low-rank adaptation ensembles. We analyze three common multiple-choice datasets using low-rank adaptation ensembles based on Mistral-7b, and draw quantitative and qualitative conclusions on their perceived complexity and balance between retained prior knowledge and domain specific adaptation during and after fine-tuning. We identify unexpected retention of acquired knowledge during fine-tuning in the overfitting regime.

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BibTeX RIS

Oleksandr Balabanov, Hampus Linander. 2024-02-19. Uncertainty quantification in fine-tuned LLMs using LoRA ensembles. https://arxiv.org/abs/2402.12264

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