Introduction to Efficient-LLMs-Survey
Overview
The project "Efficient Large Language Models: A Survey" is a comprehensive review of research aimed at improving the efficiency of large language models (LLMs). These models are known for their impressive capabilities across various tasks, potentially affecting many areas of society. However, the substantial resource requirements for these models pose significant challenges. The survey, therefore, focuses on techniques to enhance the efficiency of LLMs, categorizing the research into three main areas: model-centric, data-centric, and framework-centric methods.
Community Support
The Efficient-LLMs-Survey project is actively maintained by a team of researchers from various prestigious institutions, including The Ohio State University, Imperial College London, University of Michigan, and prominent tech companies like Amazon AWS, Google Research, and Microsoft Research Asia. The maintenance team encourages community engagement through feedback and contributions. They aim to continually update the repository with new research insights, maintaining it as a valuable resource for the research community.
Significance of Efficient LLMs
LLMs are pivotal in driving the next wave of AI evolution. They offer unparalleled performance in learning and language processing tasks but come with the trade-off of high computational costs. The resource demands are evident in both the training, which requires massive GPU hours, and inference phases, where larger models suffer from higher latency. These factors underline the necessity for efficiency-enhancing techniques. The survey highlights innovations like grouped-query attention and sliding window attention, which offer promising improvements in model throughput and efficiency.
Content Structure
The survey organizes the literature into several structured categories:
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Model-Centric Methods:
- Model Compression: Techniques in quantization, parameter pruning, and knowledge distillation.
- Efficient Pre-Training: Methods like mixed precision training and algorithm optimizations.
- Efficient Fine-Tuning: Innovations in tuning techniques to enhance parameter efficiency.
- Efficient Inference: Approaches to reduce inference time and improve latency.
- Efficient Architecture: Design strategies to enhance model performance.
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Data-Centric Methods:
- Data Selection: Criteria for selecting data across different training phases.
- Prompt Engineering: Development of effective prompting techniques to enhance model output.
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System-Level Efficiency Optimization and LLM Frameworks:
- System-level Optimization: Enhancements in training and serving efficiency.
- Frameworks: Tools and environments supporting efficient LLM use.
Model-Centric Method Highlights
Model Compression
The survey discusses advancements in making LLMs more compact through methods like post-training quantization and parameter pruning. These strategies are crucial in reducing the size of models without compromising their capabilities.
Efficient Architecture
Efforts are being made to develop more efficient model architectures that incorporate new attention mechanisms and alternative sequential models. These innovations aim to optimize how information is processed and stored, pushing the boundaries of what LLMs can achieve.
Contribution to AI Research
By structuring these insights, the survey aims to assist both researchers and industry practitioners in understanding and tackling the efficiency challenges of LLMs. It serves as an inspiration and a guide to further contributions in this dynamic and evolving field. The survey emphasizes not only performance improvements but also the importance of accessible and cost-effective model deployment to a broader range of applications and user bases.
In summary, the Efficient-LLMs-Survey project is a critical resource for anyone invested in the development and deployment of large language models, highlighting the necessity for efficiency and scalability in AI advancements.