LLMs for automatic annotation of Mandarin narrative transcripts

In three linesStudy evaluating 4 LLMs for automatic annotation of narrative macrostructure in spoken Mandarin (MAIN). Best model achieves k=0.794 vs k=0.872 (human-human), reduces annotation time by 65%, but struggles with lexical variation and semantic ambiguity. Prompt templates open-sourced.

Summary generated by Claude — human-verified