Practical Solutions and Value of Large Language Models (LLMs)
Challenges in Large-Scale Language Models
Large language models (LLMs) in natural language processing (NLP) pose challenges in computational resources and memory usage, limiting accessibility for researchers.
Optimization and Acceleration Techniques
Recent studies have developed frameworks, libraries, and techniques to overcome challenges in training and managing large-scale LLMs, providing valuable insights for researchers seeking optimal language models.
Systematic Literature Review (SLR)
Researchers conducted a comprehensive SLR analyzing 65 publications, focusing on optimizing and accelerating LLMs without sacrificing accuracy, providing a taxonomy for improving LLMs based on three classes: training, inference, and system serving.
Frameworks and Libraries for LLM Training
Key frameworks and libraries such as GPipe, ByteTransformer, Megatron-LM, LightSeq2, and CoLLiE help overcome LLM training limitations, achieving state-of-the-art results on NLP tasks with high throughput.
Challenges in LLM Inference and Practical Solutions
LLM inference frameworks and libraries address challenges such as computational expenses, resource constraints, and the requirement of balance speed, accuracy, and resource utilization through hardware specialization, resource optimization, algorithmic improvements, and distributed inference.
Optimization Techniques for LLMs
Diverse optimization techniques for LLMs have been developed, including algorithmic, model partitioning, fine-tuning for efficiency, scheduler optimization, and other optimizations such as size reduction, parallelism strategies, memory optimization, heterogeneous optimization, and automatic parallelism.
Limitations and Future Research
While the SLR on large language model optimization techniques is thorough, it has some limitations, emphasizing the need for future research to fully realize the potential of LLM optimization techniques.
AI Solutions and Business Impact
AI solutions can redefine work processes, identify automation opportunities, define KPIs, select appropriate tools, and implement AI gradually to stay competitive and evolve companies.
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