[25]: Parameter-efficient fine-tuning of large-scale pre-trained language models, Nature Machine Intelligence, vol. 5, no. 3, pp. 220–235, 2023. [26]: Parameter efficient fine-tuning methods for pretrained lan
With the prevalence of pre-trained language models (PLMs) and the pre-training–fine-tuning paradigm, it has been continuously shown that larger models tend to yield better performance. However, as PLMs scale up, fine-tuning and storing all the parameter
Parameter-efficient finetuning stands at the forefront of this pursuit, allowing researchers and practitioners to reuse pretrained models while minimizing their computational and resource footprints. It also allows us to train AI models on a broader range of hardware, including devices with limited compu...
S-BitFit【Neural architecture search for parameter-efficient fine-tuning of large pre-trained language models】将 NAS 应用到 Bitfit,保留了 Bitfit 中的结构化性质,限制 NAS 算法必须为每个 bias 模块选择是否倒数为的。 Xattn Tuning其仅微调交叉注意层。 SPT (sensitivity-aware visual parameter-efficient ...
本文参考论文《An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models》 摘要 GPT-3 和 ChatGPT 等大型语言模型 (LLM) 的成功导致了众多具有成本效益且易于访问的替代方案的开发,这些替代方案是通过使用特定于任务的数据(例如 ChatDoctor)或指令数据(例如,Alpaca)。在各种微调方法中,基于ad...
Index Terms: Large Language Model, Parameter-Efficient Fine-tuning, Computer System, Distributed System. 关键词:大型语言模型、参数高效微调、计算机系统、分布式系统。 1、Introduction Large Models (LMs) have recently captured considerable public interest. Their ability to understand context and nuances enable...
LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models (arXiv 2304.01933) Prefix-Tuning: Optimizing Continuous Prompts for Generation (ACL 2021) The Power of Scale for Parameter-Efficient Prompt Tuning (EMNLP 2021) GPT Understands, Too (arXiv 2103.10385) P-Tun...
To address this issue, parameter-efficient fine-tuning (PEFT) offers a practical solution by efficiently adjusting the parameters of large pre-trained models to suit various downstream tasks. Specifically, PEFT adjusts the parameters of pre-trained large language models to adapt to specific tasks or...
Paper tables with annotated results for Layer-wise Importance Matters: Less Memory for Better Performance in Parameter-efficient Fine-tuning of Large Language Models
The fine-tuning of Large Language Models (LLMs) is pivotal for achieving optimal performance across diverse downstream tasks. However, while full fine-tuning delivers superior results, it entails significant computational and resource costs. Parameter-Efficient Fine-Tuning (PEFT) methods, such as LoRA...