The article details a dual-phase approach consisting of taxonomy generation and LLM-augmented text classification. In the taxonomy generation phase, the TnT-LLM framework's performance is analyzed across languages, revealing consistent accuracy in both English and non-English contexts. The subsequent label assignment phase examines the agreement between human annotators and GPT-4, highlighting significant debates in user intent classifications. Findings suggest that while language models contribute to intent classification, humans offer invaluable insights, particularly for queries with subtle distinctions.
The agreement results between different pairs of human annotators and the LLM annotator indicate that misalignment occurs primarily in interpreting user intent categories.
Our findings suggest that while LLMs can classify user intents, human annotators still provide critical insights, especially for nuanced queries requiring deeper understanding.
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