Complete Categorization of Instinct-Exploiting Data-Explanations and Their Generation with Large-Language Models

Abstract

This paper proposes a complete categorization of instinct-exploiting data-explanations and their generation with Large-Language Models (LLMs). Zhang et al. proposed such explanations that are unethical, credible, and exploit some of the ten human instincts in the world best-seller book Factfulness. They also proposed four judgment methods based on phrase embedding. Unlike their work, our categorization is complete in the sense that any explanation belongs to one of its four categories. Moreover, it clarifies the human instincts that are effective in each category, which deepens our understanding on such disinformation. Our generation method effectively uses our categorization and prompt engineering to yield such data-explanations despite the guardrails of LLMs. Extensive experiments prove the effectiveness of our generation method together with insights to prevent such explanations and findings toward an automatic evaluation.