The Moral Gap of Large Language Models

• URL: http://arxiv.org/abs/2507.18523v1
• Date: Thu, 24 Jul 2025 15:49:06 GMT
• Title: The Moral Gap of Large Language Models
• Authors: Maciej Skorski, Alina Landowska,
• Abstract summary: Moral foundation detection is crucial for analyzing social discourse and developing ethically-aligned AI systems.<n>This study provides the first comprehensive comparison between state-of-the-art LLMs and fine-tuned transformers across Twitter and Reddit datasets using ROC, PR, and DET curve analysis.<n>Results reveal substantial performance gaps, with LLMs exhibiting high false negative rates and systematic under-detection of moral content despite prompt engineering efforts.
• Score: 1.568356637037272
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Moral foundation detection is crucial for analyzing social discourse and developing ethically-aligned AI systems. While large language models excel across diverse tasks, their performance on specialized moral reasoning remains unclear. This study provides the first comprehensive comparison between state-of-the-art LLMs and fine-tuned transformers across Twitter and Reddit datasets using ROC, PR, and DET curve analysis. Results reveal substantial performance gaps, with LLMs exhibiting high false negative rates and systematic under-detection of moral content despite prompt engineering efforts. These findings demonstrate that task-specific fine-tuning remains superior to prompting for moral reasoning applications.

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