The article discusses the process of label assignment using user intent taxonomy and conversation domain taxonomy, referencing Tables 5 and 6. The majority of label names and descriptions are generated through an automated TnT-LLM framework, which ensures efficiency. However, a lightweight calibration process involving human oversight is incorporated to enhance these taxonomies' reliability. Additionally, illustrative examples are included, although they do not directly correlate with specific data points in the underlying corpus, rather serving solely for illustration purposes.
The label assignment phase like user intent taxonomy and conversation domain taxonomy relies on TnT-LLM framework for automatic generation, supplemented by human calibration.
Human calibration enhances automatically generated taxonomies, ensuring their accuracy; however, examples added serve illustrative purposes with no direct relation to the actual corpus.
#label-assignment #user-intent-taxonomy #conversation-domain-taxonomy #machine-learning #human-calibration
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