SLM与LLM：企业决策指南及真实成本数据与基准

Research indicates that fine-tuned small language models outperform zero-shot GPT-4 in the majority of classification tasks. The LoRA Land study tested 310 fine-tuned models across 31 tasks, finding that these models beat GPT-4 on approximately 25 tasks with an average improvement of 10 points. Separate research from Predibase's Fine-tuning Index showed enhancements of 25-50% on specialized tasks. These findings suggest that while large language models (LLMs) like GPT-4 are highly regarded, small language models (SLMs) may offer greater advantages in specific applications. For instance, Air Canada's chatbot even invented a refund policy, showcasing the potential of fine-tuned models in real-world applications.