A step-by-step method for cultural annotation by LLMs - Dubourg, Edgar; Thouzeau, Valentin; Baumard, Nicolas (Frontiers in Artificial Intelligence, 2024)
Building on the growing body of research highlighting the capabilities of Large Language Models (LLMs) like Generative Pre-trained Transformers (GPT), this paper presents a structured pipeline for the annotation of cultural (big) data through such LLMs, offering a detailed methodology for leveraging GPT's computational abilities. Our approach provides researchers across various fields with a method for efficient and scalable analysis of cultural phenomena, showcasing the potential of LLMs in the empirical study of human cultures. LLMs proficiency in processing and interpreting complex data finds relevance in tasks such as annotating descriptions of non-industrial societies, measuring the importance of specific themes in stories, or evaluating psychological constructs in texts across societies or historical periods. These applications demonstrate the model's versatility in serving disciplines like cultural anthropology, cultural psychology, cultural history, and cultural sciences at large.