Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning

• URL: http://arxiv.org/abs/2303.15647v2
• Date: Fri, 22 Nov 2024 05:02:26 GMT
• Title: Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning
• Authors: Vladislav Lialin, Vijeta Deshpande, Xiaowei Yao, Anna Rumshisky,
• Abstract summary: This paper presents a systematic overview of parameter-efficient fine-tuning methods, covering over 50 papers published between early 2019 and mid-2024. We provide a taxonomy that covers a broad range of methods and present a detailed method comparison. We also conduct an extensive head-to-head experimental comparison of 15 diverse PEFT methods, evaluating their performance and efficiency on models up to 11B parameters.
• Score: 10.51168925267033
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• Abstract: This paper presents a systematic overview of parameter-efficient fine-tuning methods, covering over 50 papers published between early 2019 and mid-2024. These methods aim to address the challenges of fine-tuning large language models by training only a small subset of parameters. We provide a taxonomy that covers a broad range of methods and present a detailed method comparison with a specific focus on real-life efficiency in fine-tuning multibillion-scale language models. We also conduct an extensive head-to-head experimental comparison of 15 diverse PEFT methods, evaluating their performance and efficiency on models up to 11B parameters. Our findings reveal that methods previously shown to surpass a strong LoRA baseline face difficulties in resource-constrained settings, where hyperparameter optimization is limited and the network is fine-tuned only for a few epochs. Finally, we provide a set of practical recommendations for using PEFT methods and outline potential future research directions.

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