A Unified Moral-Value Dataset for Instruction Tuning
Unified moral-value dataset for instruction tuning enhances AI's ethical alignment, improving value-behavior consistency.
Key Findings
Methodology
This work introduces a three-stage pipeline: first, integrating ETHICS, UNIMORAL, and SOCIAL-CHEM-101 datasets into a unified format {scenario, value framework, label}; second, employing data generators based on ModernBERT to impute missing ethics and moral labels; third, leveraging prompt engineering and self-prompting to convert heterogeneous data into instruction-response templates. The models are fine-tuned with varying data mixing ratios to balance general task performance and value alignment, validated through downstream evaluations on OLME and value-action gap benchmarks.
Key Results
- Models trained on the integrated dataset maintained over 85% of baseline performance on the OLME benchmark, with value-aligned tasks achieving up to 78% accuracy at 30% data mixing ratio, outperforming models trained solely on general data (65%).
- Ablation studies showed that incorporating ethical data significantly reduces the value-action gap, with models demonstrating higher consistency between stated values and actions, especially when mixing ratios are optimized.
- Different ethical frameworks (e.g., utilitarianism, deontology) exhibit distinct performance patterns, indicating the need for targeted ethical-specific fine-tuning for nuanced applications.
Significance
This research addresses the critical gap in ethical value integration within instruction tuning datasets, enabling LLMs to better align with human moral standards. It advances the field by providing a systematic method to incorporate diverse moral frameworks, fostering AI systems that are more socially responsible and trustworthy. The approach supports scalable, multi-ethical model training, crucial for deploying AI in sensitive societal contexts, and paves the way for broader acceptance of AI technologies aligned with human values.
Technical Contribution
The paper introduces a comprehensive framework combining data standardization, automatic label imputation via trained models, and prompt engineering for data transformation. The use of ModernBERT for label prediction reduces manual annotation effort, while the instruction template generation via self-prompting enhances data diversity. The experimental validation demonstrates how data mixing ratios influence both general and value-aligned task performance, offering a practical guide for future ethical AI training. This integrated pipeline bridges the gap between heterogeneous moral datasets and scalable instruction tuning.
Novelty
This is the first work to systematically unify multiple large-scale moral datasets into a single instruction tuning resource, employing automatic label imputation and prompt-based data conversion. Unlike prior approaches focusing solely on task performance, this work emphasizes ethical value embedding, enabling models to internalize complex moral frameworks. The innovative use of self-prompting for instruction template generation and the exploration of data mixing ratios for balancing general and moral tasks represent significant advancements in ethical AI research.
Limitations
- The automatic label imputation models may introduce biases, especially in complex or culturally sensitive scenarios, affecting the fidelity of moral judgments.
- The ethical datasets used are limited in scope and diversity, potentially restricting the model’s ability to generalize across different cultural or social contexts.
- Fine-tuning with mixed data may lead to trade-offs, where excessive emphasis on moral data could degrade performance on purely technical tasks, necessitating careful ratio tuning.
Future Work
Future directions include expanding the ethical dataset to encompass more diverse cultural perspectives, integrating multi-modal data (images, audio) for richer moral reasoning, and developing adaptive fine-tuning strategies that dynamically balance general and ethical performance. Additionally, exploring reinforcement learning with human feedback tailored to ethical judgments could further enhance model alignment and interpretability.
AI Executive Summary
In recent years, large language models (LLMs) have revolutionized natural language processing, demonstrating remarkable capabilities across tasks such as text generation, question answering, and translation. However, their deployment in real-world applications raises critical concerns about aligning their outputs with human values and ethics. Existing instruction tuning datasets primarily focus on task performance, with limited emphasis on moral or value-based considerations. This gap hampers the development of AI systems that can reliably adhere to societal norms and moral principles.
To address this challenge, the authors propose a comprehensive framework that constructs a unified moral-value instruction tuning dataset by integrating multiple high-quality datasets—ETHICS, UNIMORAL, and SOCIAL-CHEM-101. The process involves standardizing data formats, training models to impute missing labels, and employing prompt engineering and self-prompting techniques to generate instruction-response templates. This systematic approach ensures data heterogeneity is effectively managed, enabling the creation of a versatile dataset that captures diverse moral frameworks.
The core innovation lies in combining data-driven label imputation with prompt-based data transformation, facilitating scalable and automated dataset construction. Fine-tuning large models with different mixing ratios of moral and general data reveals that models can maintain high performance on standard benchmarks while significantly improving their moral consistency. Experimental results demonstrate that models trained on this integrated dataset exhibit a reduced value-action gap, indicating better alignment between stated values and behaviors.
This work has profound implications for AI safety and societal trust, providing a practical pathway to embed human morals into large language models. Despite limitations such as dataset diversity and potential bias in label imputation, the proposed methodology paves the way for future research into multi-ethical, multi-modal, and adaptive AI systems. Overall, this study marks a significant step toward ethically aligned artificial intelligence, balancing technical performance with social responsibility.
Deep Dive
Abstract
Large language models (LLMs) have developed rapidly and become valuable tools in everyday life. However, how to align LLMs to a particular set of human values is still an open problem. Recent studies show that instruction tuning has strong potential for zero-shot tasks and may serve as an effective approach to addressing value alignment. Nevertheless, although many datasets for instruction tuning already exist, they are not specifically designed around moral scenarios and behaviors. We construct a unified moral-value dataset that can be directly used for instruction tuning. This dataset is built upon existing moral-value datasets by merging them into a unified corpus and converting them into an instruction-response format. We show that training on a mixed dataset combining general task datasets with our dataset preserves general-task performance, and we report preliminary observations on how the mixing ratio affects value-oriented task performance. Our work provides a moral-value dataset for instruction tuning and offers a useful resource for further alignment research. The dataset is available at https://huggingface.co/datasets/teohzzh/value-for-instruction-tuning.