Bayesian Low-rank Adaptation for Large Language Models
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866914667379556352 |
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| author | Yang, Adam X. Robeyns, Maxime Wang, Xi Aitchison, Laurence |
| author_facet | Yang, Adam X. Robeyns, Maxime Wang, Xi Aitchison, Laurence |
| contents | Low-rank adaptation (LoRA) has emerged as a new paradigm for cost-efficient fine-tuning of large language models (LLMs). However, fine-tuned LLMs often become overconfident especially when fine-tuned on small datasets. Bayesian methods, with their inherent ability to estimate uncertainty, serve as potent tools to mitigate overconfidence and enhance calibration. In this work, we introduce Laplace-LoRA, which applies a Bayesian approach to the LoRA parameters. Specifically, Laplace-LoRA applies a Laplace approximation to the posterior over the LoRA parameters, considerably improving the calibration of fine-tuned LLMs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_13111 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Bayesian Low-rank Adaptation for Large Language Models Yang, Adam X. Robeyns, Maxime Wang, Xi Aitchison, Laurence Machine Learning Low-rank adaptation (LoRA) has emerged as a new paradigm for cost-efficient fine-tuning of large language models (LLMs). However, fine-tuned LLMs often become overconfident especially when fine-tuned on small datasets. Bayesian methods, with their inherent ability to estimate uncertainty, serve as potent tools to mitigate overconfidence and enhance calibration. In this work, we introduce Laplace-LoRA, which applies a Bayesian approach to the LoRA parameters. Specifically, Laplace-LoRA applies a Laplace approximation to the posterior over the LoRA parameters, considerably improving the calibration of fine-tuned LLMs. |
| title | Bayesian Low-rank Adaptation for Large Language Models |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2308.13111 |