# The Moral Gap of Large Language Models

arXiv: https://arxiv.org/abs/2507.18523 · HTML: https://mlsanity.com/paper/2507.18523/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 21 references that are in the map.
- First posted on arXiv 24 Jul 2025.

## Builds on (most cited first, 5 of 5)

- [Social Chemistry 101: Learning to Reason about Social and Moral Norms](https://mlsanity.com/paper/2011.00620/) (2020)
- [Moral Framing and Ideological Bias of News](https://mlsanity.com/paper/2009.12979/) (2020)
- [MoralBERT: A Fine-Tuned Language Model for Capturing Moral Values in Social Discussions](https://mlsanity.com/paper/2403.07678/) (2024)
- [Measuring Moral Dimensions in Social Media with Mformer](https://mlsanity.com/paper/2311.10219/) (2023)
- [Mapping Topics in 100,000 Real-life Moral Dilemmas](https://mlsanity.com/paper/2203.16762/) (2022)

## Built on by (most cited first, 2 of 2)

- [Beyond Human Judgment: A Bayesian Evaluation of LLMs' Moral Values Understanding](https://mlsanity.com/paper/2508.13804/) (2025)
- [Less Is Moral: A CHARMing Framework for Moral Foundations Detection in Endorsement Behaviour](https://mlsanity.com/paper/2609.03330/) (2026)

## Abstract (arXiv)

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.