The document "In Praise of Small Language Models" explores the advantages of small language models (SLMs) over large language models (LLMs), particularly in investment applications. While LLMs drive AI advancements, they suffer from hallucinations and opacity, making them unreliable for precise tasks. SLMs, by contrast, provide consistent, interpretable results and are cost-effective. A case study on China’s housing market demonstrates how SLMs can analyze financial risks effectively. The paper argues that despite the dominance of LLMs, SLMs remain valuable tools for tasks requiring accuracy, transparency, and efficiency, particularly in financial and investment sectors.

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