Parameter-Efficient Fine-Tuning Strategies for Large Language Models
Large Language Models (LLMs) represent a significant advancement in various fields, enabling remarkable achievements in diverse tasks. However, their large size requires substantial computational resources. Adapting them to specific tasks is challenging due to their scale and computational requirements, particularly on limited hardware platforms.
Practical Solutions and Value:
- Zero-shot learning allows LLMs to apply knowledge to new tasks not encountered during training.
- Parameter-Efficient Fine-Tuning (PEFT) optimizes LLM performance on user datasets and tasks, extending beyond Natural Language Processing (NLP) to computer vision (CV) and interdisciplinary vision-language models.
- Thorough examination of diverse PEFT algorithms, evaluating their performance, computational requirements, and real-world system designs.
This survey equips researchers with insights into PEFT algorithms and their system implementations, offering detailed analyses of recent progressions and practical uses.
Categorized PEFT Algorithms:
The researchers categorized PEFT algorithms into additive, selective, reparametrized, and hybrid fine-tuning based on their operations.
Computation and Memory Considerations:
- Analysis of computation costs and memory overhead in LLMs.
- Optimizing LLM inference process through strategies like KeyValue cache (KV-cache) for storing previous Keys and Values.
Conclusion:
This survey provides invaluable guidance for researchers in the complexities of fine-tuning large models, exploring diverse PEFT algorithms and their implementation costs.
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