The article discusses creating a benchmark dataset for reliable text classification evaluation through human annotation and LLM integration. The process begins with assessing label assignment reliability using Cohen's and Fleiss' Kappa, allowing for a consensus among human raters. Disagreements are resolved before incorporating an LLM as an annotator to compare its performance against human consensus. The evaluation employs conventional classification metrics to quantify alignment between human and LLM annotations, providing insights on the effectiveness of the classifiers on both small and larger data samples.
To ensure reliability in text classification, we utilize multiple human annotators to achieve a consensus on label assignments, measured by Cohen's Kappa and Fleiss' Kappa.
Integrating a large language model (LLM) as an annotator, we assess its alignment with human consensus in label assignments, enhancing the reliability of our evaluation methods.
#text-classification #annotation-reliability #large-language-models #evaluation-metrics #machine-learning
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