This article presents the Interpretable CrossExamination Technique (ICE-T), an innovative prompting method that merges large language model (LLM) responses with traditional classifiers. The technique specifically aims to improve binary classification tasks by navigating the limitations of zero-shot and few-shot learning. ICE-T utilizes a structured multi-prompt framework that quantifies qualitative data, thus facilitating more effective classification. Results indicate ICE-T outperforms conventional zero-shot methods across various datasets and metrics, especially in contexts where understanding model decisions is critical, proposing a shift towards automated and accessible AI systems for broader user engagement.
The Interpretable CrossExamination Technique (ICE-T) addresses the limitations of zero-shot learning by transforming qualitative data into quantifiable metrics, enhancing binary classification performance.
ICE-T consistently improves performance over standard zero-shot baselines, particularly showcasing its utility in clinical applications where model interpretability is essential for decision-making.
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