Aligning AI With Shared Human Values
Dan Hendrycks, Collin Burns, Steven Basart, Andrew Critch, Jerry Li, Dawn Song, Jacob Steinhardt
Abstract
We show how to assess a language model's knowledge of basic concepts of morality. We introduce the ETHICS dataset, a new benchmark that spans concepts in justice, well-being, duties, virtues, and commonsense morality. Models predict widespread moral judgments about diverse text scenarios. This requires connecting physical and social world knowledge to value judgements, a capability that may enable us to steer chatbot outputs or eventually regularize open-ended reinforcement learning agents. With the ETHICS dataset, we find that current language models have a promising but incomplete ability to predict basic human ethical judgements. Our work shows that progress can be made on machine ethics today, and it provides a steppingstone toward AI that is aligned with human values.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Cited by top-tier papers178
- Measuring Massive Multitask Language UnderstandingDan Hendrycks, Collin Burns, Steven Basart, Andy Zou et al.ICLR 2021 · 7,905 citations
- Tree of Attacks: Jailbreaking Black-Box LLMs AutomaticallyAnay Mehrotra, Manolis Zampetakis, Paul Kassianik, Blaine Nelson et al.NeurIPS 2024 · 835 citations
- Towards Understanding and Mitigating Social Biases in Language ModelsPaul Pu Liang, Chiyu Wu, Louis-Philippe Morency, Ruslan SalakhutdinovICML 2021 · 495 citations
- Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak SupervisionCollin Burns, Pavel Izmailov, Jan Hendrik Kirchner, Bowen Baker et al.ICML 2024 · 443 citations
- The WMDP Benchmark: Measuring and Reducing Malicious Use with UnlearningNathaniel Li, Alexander Pan, Anjali Gopal, Summer Yue et al.ICML 2024 · 390 citations
Builds on7
- Language Models are Few-Shot LearnersTom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah et al.NeurIPS 2020 · 64,255 citations
- ALBERT: A Lite BERT for Self-supervised Learning of Language RepresentationsZhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel et al.ICLR 2020 · 7,418 citations
- PIQA: Reasoning about Physical Commonsense in Natural LanguageYonatan Bisk, Rowan Zellers, Ronan Le Bras, Jianfeng Gao et al.AAAI 2020 · 2,916 citations
- Reformer: The Efficient TransformerNikita Kitaev, Lukasz Kaiser, Anselm LevskayaICLR 2020 · 2,878 citations
- Learning The Difference That Makes A Difference With Counterfactually-Augmented DataDivyansh Kaushik, Eduard H. Hovy, Zachary Chase LiptonICLR 2020 · 625 citations
Related papers
- MoCa: Measuring Human-Language Model Alignment on Causal and Moral Judgment TasksAllen Nie, Yuhui Zhang, Atharva Amdekar, Chris Piech et al.NeurIPS 2023 · 78 citations
- Large Language Models Often Say One Thing and Do AnotherRuoxi Xu, Hongyu Lin, Xianpei Han, Jia Zheng et al.ICLR 2025
- Do Morals Guide How LLMs Think? The Role of Ethical Perspectives in General Problem SolvingIseo Kim, Eunjin Hong, Juae KimACL 2026
- The Moral Integrity Corpus: A Benchmark for Ethical Dialogue SystemsCaleb Ziems, Jane A. Yu, Yi-Chia Wang, Alon Y. Halevy et al.ACL 2022 · 127 citations
- SCRUPLES: A Corpus of Community Ethical Judgments on 32, 000 Real-Life AnecdotesNicholas Lourie, Ronan Le Bras, Yejin ChoiAAAI 2021 · 147 citations
