Speakers
Description
This study explores the use of several chatbots based on recent generative large language models for automatic term extraction (ATE) from smaller text samples. The samples were selected from three domains: board games, ice hockey, and kitesurfing; and they cover three languages: English, French, and Portuguese. We used four prompting strategies: zero shot, one shot, few shots, and few shots with context. A single prompt with placeholders for language, domain and examples (when available) was used for all settings, and, in the case of French and Portuguese, we tested the ATE prompt in English and in the respective language. Results were calculated in terms of f-measure, and we further tested the best models with five consecutive runs to calculate a mean fmeasure and a standard deviation. No clear best system was verified for the task. Each of the domains and languages had different best systems. In terms of prompting strategy, more information did not always lead to better results, as zero-shot and one-shot attempts had the best results in several scenarios. The main contribution of the study is an overview of the ATE capacity of several chatbot systems across multiple scenarios.
