The LLM Hype Bubble Bursts: The Rise of Small Language Models
The initial excitement surrounding large language models (LLMs) is fading, with many companies yet to see a return on investment. The author argues that we've been fooled by LLMs' fluent language, mistaking it for genuine intelligence. The future, they suggest, lies in smaller, more distributed models, mirroring the evolution of dynamo technology. Small language models (SLMs) will focus on smaller, more specific language tasks, such as query rewriting, rather than attempting to mimic human intelligence. This will lower costs, increase efficiency, and reduce ethical concerns. Instead of pursuing 'intelligent' applications, the author advocates using LLMs for their strengths in low-level language processing, such as proofreading and text summarization. This, they argue, is the true path forward for LLMs.
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