AI-driven speech act annotation: accuracy and reproducibility across ChatGPT, LadderWeb and LLaMA

Abstract

This study evaluates three machine learning systems for annotating pragmatic categories, focusing on cancellations after accepting an invitation. The systems include the supervised model LadderWeb and the pre-trained models ChatGPT-4o and LLaMA-3.2. LadderWeb, built on Apache OpenNLP, was specifically designed for cancellation annotation. ChatGPT-4o was tested through a web interface to simulate non-expert use, while LLaMA-3.2 was run locally to ensure control, reproducibility, and data security. Both large language models were prompted using a few-shot learning approach (Brocca et al., in review). System outputs were compared against a human baseline. GPT achieved the highest agreement across dimensions, with κ values ranging from substantial to almost perfect. LadderWeb also showed substantial agreement, whereas LLaMA performed considerably worse. Repeated testing after seven months revealed that GPT’s results varied, though accuracy remained high, while LadderWeb and LLaMA produced self-consistent outputs. Notably, LLaMA improved when parameters were adjusted. These findings highlight the potential of pre-trained large language models such as ChatGPT-4o to support pragmatic corpus annotation, while also emphasizing their reproducibility challenges—an issue not observed with LadderWeb or LLaMA.

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