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Evaluating LLMs for Multi-label Text Classification
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DOI：10.1007/978-981-95-3055-7_24.png)
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Abstract
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As machine learning models grow in size, the demand for well-annotated data increases. However, human annotation is expensive, and the human-labeling process faces issues such as delayed response and ethical concerns. The recently launched ChatGPT provides an alternative solution to generate labels instead of using human annotators. This paper explores ChatGPT's potential to replace human efforts in text classification tasks through a comprehensive investigation. Our findings reveal that ChatGPT can perform well in text classification tasks, though fairness issues require attention. These results demonstrate the potential of ChatGPT in replacing human annotators, especially in ethically challenging, content-sensitive tasks where human involvement could be limited.
Keywords:
Active Learning
Large Language Models (LLMs)
Multi-label Text Classification
Human-in-the-loop
Fairness and Bias
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Testing the Reliability of ChatGPT for Text Annotation and Classification: A Cautionary Remark.png)
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