Interpretable Cross-Examination Technique (ICE-T): Using highly informative features to boost LLM performance
<|reference_start|>Interpretable Cross-Examination Technique (ICE-T): Using highly informative features to boost LLM performance: In this paper, we introduce the Interpretable Cross-Examination Technique (ICE-T), a novel approach that leverages structured multi-prompt techniques with Large Language Models (LLMs) to improve classification performance over zero-shot and few-shot methods. In domains where interpretability is crucial, such as medicine and law, standard models often fall short due to their "black-box" nature. ICE-T addresses these limitations by using a series of generated prompts that allow an LLM to approach the problem from multiple directions. The responses from the LLM are then converted into numerical feature vectors and processed by a traditional classifier. This method not only maintains high interpretability but also allows for smaller, less capable models to achieve or exceed the performance of larger, more advanced models under zero-shot conditions. We demonstrate the effectiveness of ICE-T across a diverse set of data sources, including medical records and legal documents, consistently surpassing the zero-shot baseline in terms of classification metrics such as F1 scores. Our results indicate that ICE-T can be used for improving both the performance and transparency of AI applications in complex decision-making environments.<|reference_end|>
@article{muric2024interpretable,
    title={Interpretable Cross-Examination Technique (ICE-T): Using highly
  informative features to boost LLM performance},
    author={Goran Muric, Ben Delay, Steven Minton},
    journal={arXiv preprint arXiv:2405.06703},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.06703},
    primaryClass={cs.CL cs.AI cs.LG}
}