Interpretable Cross-Examination Technique (ICE-T): Using highly informative features to boost LLM performance

• URL: http://arxiv.org/abs/2405.06703v1
• Date: Wed, 8 May 2024 19:20:34 GMT
• Title: Interpretable Cross-Examination Technique (ICE-T): Using highly informative features to boost LLM performance
• Authors: Goran Muric, Ben Delay, Steven Minton,
• Abstract summary: 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. We demonstrate the effectiveness of ICE-T across a diverse set of data sources, including medical records and legal documents.
• Score: 1.1961645395911131
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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