How Big Is Too Big for An LLM? – Towards AI

Summary: Dr. Leon Eversberg discusses the rapid growth of Large Language Models (LLMs) in terms of size and parameters over the years. The evolution of LLMs, specifically GPT models, shows a significant increase in size with each new iteration. The performance of LLMs heavily depends on scale, including the number of model parameters, training dataset size, and computation resources. However, the challenge lies in balancing the need for larger models with the limitations of memory, training data availability, and high computational costs. Google DeepMind researchers have found that many LLMs are undertrained, indicating a mismatch between model size and training data. Ultimately, optimizing large language models for compute efficiency is crucial for improving performance and overcoming resource constraints in AI research. The full blog can be read for free on Medium via Towards AI.

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