Are LLMs complicated ethical dilemma analyzers?

TL;DR

This paper evaluates whether large language models can emulate human ethical reasoning by benchmarking their responses against expert opinions on real-world dilemmas, revealing strengths and limitations in their moral judgment capabilities.

Contribution

Introduces a novel benchmark dataset and a comprehensive evaluation framework for assessing LLMs' ethical reasoning against expert responses.

Findings

LLMs outperform non-experts in lexical and structural similarity

GPT-4o-mini shows the most consistent performance

Models struggle with historical grounding and nuanced resolution strategies

Abstract

One open question in the study of Large Language Models (LLMs) is whether they can emulate human ethical reasoning and act as believable proxies for human judgment. To investigate this, we introduce a benchmark dataset comprising 196 real-world ethical dilemmas and expert opinions, each segmented into five structured components: Introduction, Key Factors, Historical Theoretical Perspectives, Resolution Strategies, and Key Takeaways. We also collect non-expert human responses for comparison, limited to the Key Factors section due to their brevity. We evaluate multiple frontier LLMs (GPT-4o-mini, Claude-3.5-Sonnet, Deepseek-V3, Gemini-1.5-Flash) using a composite metric framework based on BLEU, Damerau-Levenshtein distance, TF-IDF cosine similarity, and Universal Sentence Encoder similarity. Metric weights are computed through an inversion-based ranking alignment and pairwise AHP analysis, enabling fine-grained comparison of model outputs to expert responses. Our results show that LLMs generally outperform non-expert humans in lexical and structural alignment, with GPT-4o-mini performing most consistently across all sections. However, all models struggle with historical grounding and proposing nuanced resolution strategies, which require contextual abstraction. Human responses, while less structured, occasionally achieve comparable semantic similarity, suggesting intuitive moral reasoning. These findings highlight both the strengths and current limitations of LLMs in ethical decision-making.