MoralBench: Moral Evaluation of LLMs (2406.04428v2)

Abstract: In the rapidly evolving field of artificial intelligence, LLMs have emerged as powerful tools for a myriad of applications, from natural language processing to decision-making support systems. However, as these models become increasingly integrated into societal frameworks, the imperative to ensure they operate within ethical and moral boundaries has never been more critical. This paper introduces a novel benchmark designed to measure and compare the moral reasoning capabilities of LLMs. We present the first comprehensive dataset specifically curated to probe the moral dimensions of LLM outputs, addressing a wide range of ethical dilemmas and scenarios reflective of real-world complexities. The main contribution of this work lies in the development of benchmark datasets and metrics for assessing the moral identity of LLMs, which accounts for nuance, contextual sensitivity, and alignment with human ethical standards. Our methodology involves a multi-faceted approach, combining quantitative analysis with qualitative insights from ethics scholars to ensure a thorough evaluation of model performance. By applying our benchmark across several leading LLMs, we uncover significant variations in moral reasoning capabilities of different models. These findings highlight the importance of considering moral reasoning in the development and evaluation of LLMs, as well as the need for ongoing research to address the biases and limitations uncovered in our study. We publicly release the benchmark at https://drive.google.com/drive/u/0/folders/1k93YZJserYc2CkqP8d4B3M3sgd3kA8W7 and also open-source the code of the project at https://github.com/agiresearch/MoralBench.

• The paper introduces MoralBench, a benchmark that evaluates LLMs' moral reasoning using binary and comparative assessments based on Moral Foundations Theory.
• The paper employs binary assessments and comparative tasks to align model responses with human moral judgments, revealing that models like LLaMA-2 and GPT-4 excel overall yet struggle with nuanced comparisons.
• The paper highlights the need for improved LLM training with enhanced contextual understanding to ensure reliable ethical decision-making in high-stakes applications.

Moral Evaluation of LLMs

Introduction

The paper "MoralBench: Moral Evaluation of LLMs" (2406.04428) introduces a framework for assessing the moral reasoning capabilities of LLMs. As these models increasingly permeate sectors such as healthcare, legal systems, and education, ensuring their actions align with societal moral standards becomes critical. The authors propose a novel benchmark dataset specifically curated to evaluate the moral identity of LLMs across various ethical dilemmas. This benchmark aims to fill the current gap in systematically evaluating the ethical alignment of these models, which is essential to prevent unethical decision-making and ensure alignment with human values.

Figure 1: Data Pipeline. We have two datasets in our benchmark. Each dataset contains many moral statements. We rank these moral statements and split them into stage 1 and stage 2 to obtain and evaluate the moral identity results of the LLM in different dimensions.

Benchmark and Methodology

The benchmark developed in this paper is grounded in Moral Foundations Theory, which posits that several core moral values are universally recognized across cultures. These values include Care/Harm, Fairness/Cheating, Loyalty/Betrayal, Authority/Subversion, and Sanctity/Degradation, later expanded to include Liberty/Oppression. Utilizing this theory, the paper introduces datasets designed to evaluate how well LLMs can reflect these moral dimensions.

The evaluation method is structured in two parts: Binary Moral Assessment and Comparative Moral Assessment. The first part scores LLM responses to moral statements on a binary agree/disagree basis, correlated with human judgment scores. The second part requires models to select the more moral statement between paired options, with correctness determined by alignment with human scores.

Figure 2: Benchmark scoring. We generate a score for each moral statement in the benchmark. The left side of the figure shows the generation process of the binary moral assessment of our benchmark, and the right side shows the comparative moral assessment.

Experimental Results

Experiments were conducted using various LLMs, including Zephyr, LLaMA-2, Gemma-1.1, GPT-3.5, and GPT-4. These models were evaluated on datasets adapted from the Moral Foundations Questionnaire and Moral Foundations Vignettes. Results revealed that LLaMA-2 and GPT-4 scored highest across both binary and comparative assessments, suggesting robust alignment with human moral judgments. However, discrepancies were noted when models exhibited high binary scores but struggled with comparative tasks, indicating potential overfitting to specific training scenarios without a deep understanding of moral principles.

Implications and Future Directions

The findings underscore the importance of comprehensive evaluations to ensure LLMs reliably embody human moral standards. By advancing methods that assess both explicit and nuanced moral reasoning, the paper provides insights into improving LLM design and training processes. Future developments could focus on enhancing the contextual understanding of models, ensuring they can generalize moral reasoning across diverse scenarios. This evolution will be crucial for deploying ethically sensitive AI systems in real-world applications.

Conclusion

"MoralBench: Moral Evaluation of LLMs" (2406.04428) contributes significantly to AI ethics by introducing a benchmark that evaluates moral reasoning capabilities in LLMs. The dual-method approach offers a nuanced understanding of model alignment with human ethical values, highlighting areas for improvement in AI training. The benchmark serves as a stepping stone towards developing ethically aware AI systems, emphasizing the need for models that can navigate complex moral landscapes effectively.