This paper provides a comprehensive review of the recent advances in Parameter Efficient Fine-Tuning (PEFT) methods for large language models. PEFT aims to strike a balance between accuracy and efficiency by selectively updating a subset of model parameters, leveraging knowledge distillation, or exploiting structural redundancy.
The key highlights of the review are:
Traditional fine-tuning methods that adjust all model parameters can be computationally expensive and memory-intensive. PEFT techniques have emerged as a solution to address these limitations.
The review examines PEFT approaches across various applications, including text generation, medical imaging, protein modeling, code review/generation, and speech synthesis. It provides a detailed comparison of different PEFT strategies and their effectiveness in reducing computational load, speeding up training, and lowering memory usage.
For commonsense and arithmetic reasoning tasks, the LoReFT method achieves state-of-the-art performance while significantly reducing the number of trainable parameters compared to other PEFT approaches.
In video-text understanding, the Alignment and Generation Adapter (AGAdapter) integrates a knowledge-sharing alignment adapter with a large language model, achieving superior performance on benchmarks like MSR-VTT and ActivityNet.
For medical imaging analysis, PEFT techniques like Adapter, BitFit, and LoRA demonstrate the ability to maintain or improve performance while using significantly fewer parameters compared to traditional fine-tuning.
In the domain of protein modeling, PEFT methods such as Adapter, BitFit, and LoRA achieve comparable or superior performance to full fine-tuning, highlighting their potential for efficient and scalable solutions in proteomics.
The review also covers the application of PEFT in code review/generation and speech synthesis, showcasing the versatility and effectiveness of these techniques across diverse domains.
Overall, the paper contributes to the understanding of PEFT's evolving landscape, guiding researchers and practitioners in overcoming the limitations of conventional fine-tuning approaches and making deep learning more accessible and adaptable.
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by Charith Chan... at arxiv.org 04-23-2024
https://arxiv.org/pdf/2404.13506.pdfDeeper Inquiries