This study explores the effectiveness of fine-tuning large language models (LLMs) for chemical text mining, focusing on five intricate tasks: compound entity recognition, reaction role labeling, metal-organic framework (MOF) synthesis information extraction, nuclear magnetic resonance (NMR) data extraction, and converting reaction paragraphs to action sequences. The fine-tuned LLMs, particularly GPT-3.5-turbo, demonstrated impressive performance, achieving exact accuracy levels ranging from 69% to 95% with minimal annotated data. These models outperformed task-adaptive pre-trained and fine-tuned models based on larger in-domain data. Notably, fine-tuned models like Mistral and Llama3 also showed competitive abilities. The study highlights the potential of fine-tuning LLMs as versatile and robust toolkits for automated data acquisition in chemical knowledge extraction, offering significant advantages in terms of versatility, robustness, and low-code capability. This approach can revolutionize chemical text mining by optimizing labor-intensive and time-consuming data collection workflows, accelerating the discovery and creation of novel substances.This study explores the effectiveness of fine-tuning large language models (LLMs) for chemical text mining, focusing on five intricate tasks: compound entity recognition, reaction role labeling, metal-organic framework (MOF) synthesis information extraction, nuclear magnetic resonance (NMR) data extraction, and converting reaction paragraphs to action sequences. The fine-tuned LLMs, particularly GPT-3.5-turbo, demonstrated impressive performance, achieving exact accuracy levels ranging from 69% to 95% with minimal annotated data. These models outperformed task-adaptive pre-trained and fine-tuned models based on larger in-domain data. Notably, fine-tuned models like Mistral and Llama3 also showed competitive abilities. The study highlights the potential of fine-tuning LLMs as versatile and robust toolkits for automated data acquisition in chemical knowledge extraction, offering significant advantages in terms of versatility, robustness, and low-code capability. This approach can revolutionize chemical text mining by optimizing labor-intensive and time-consuming data collection workflows, accelerating the discovery and creation of novel substances.