Using large language models to investigate cultural ecosystem services perceptions: A few-shot and prompt method

Abstract

The advancement of generative AI has profoundly impacted various aspects of society, including scientific research, but its application in landscape research remains underexplored. In this study, large language models are applied to analyze cultural ecosystem services, which are a key connection between humans and nature, reflecting the intangible benefits that ecosystems provide. Social media texts from the Lushan Scenic Area, known for its rich cultural ecosystem services, were analyzed. The methodology involved adapting the model using few-shot learning to classify cultural ecosystem services and associated sentiments. Prompts were specifically designed to optimize model performance. The validation process compared the performance of three base models (GLM-4-0520, ERNIE-4.0-8K, and Moonshot-v1-8k) alongside five prompts. The cultural ecosystem services within the study area were subsequently analyzed based on model outputs. The findings indicated superior performance by the Moonshot-v1-8k model, achieving 82.2 % micro-F1 and 80.3 % macro-F1. The implementation of chain-of-thought prompts and cultural ecosystem services definition prompts enhanced micro-F1 and macro-F1 by up to 6.3 % and 3.3 %, respectively. Within the Lushan Scenic Area, aesthetic services were identified as the most frequently perceived, while recreational services received the most negative sentiments. A marked increase in public interest in physical health was observed following the COVID-19 pandemic. This study highlights the potential of large language models to advance the analysis of cultural ecosystem services and landscape perceptions. By offering a novel approach to text analysis, the findings contribute valuable insights for landscape management and underscore the utility of AI technologies.