Multi-label classification is a supervised Machine Learning problem, which can assign zero or more mutually non-exclusive class labels for an instance. It is different from the multi-class classification which assigns exactly one class label out of many predefined class labels for an instance. In this paper, we explore both proprietary and open-source generative Large Language Models (LLMs) for multi-label classification problems. Specifically, we fine-tune these LLMs and provide insights into their behaviors with different prompts and training constraints such as few-shots settings in Aviation Safety and Autonomy domains. We provide recommendations of choosing LLMs for multi-label classifications.


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    Title :

    Multi-Label Classification with Generative Large Language Models


    Contributors:


    Publication date :

    2024-09-29


    Size :

    212203 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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