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Language models still struggle on moral reasoning, despite their impressive performance in many other tasks.
The trolley problem
Thomson, J. J · 1984
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Thought experiments: State of the art
Stuart, M. T., Fehige, Y., and Brown, J. R · 2017
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Moral reasoning, Aug 2018
Richardson, H. S · 2018
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Thought experiments, Sep 2019
Brown, J. R. and Fehige, Y · 2019
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Language models are few-shot learners
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al · 2020
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Social chemistry 101: Learning to reason about social and moral norms
Forbes, M., Hwang, J. D., Shwartz, V., Sap, M., and Choi, Y · 2020
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Can machines learn morality? the delphi experiment
Jiang, L., Hwang, J. D., Bhagavatula, C., Le Bras, R., Liang, J., Dodge, J., Sakaguchi, K., Forbes, M., Borchardt, J., Gabriel, S., et al · 2021
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Scruples: A corpus of community ethical judgments on 32,000 real-life anecdotes
Lourie, N., Le Bras, R., and Choi, Y · 2021
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A word on machine ethics: A response to jiang et al.(2021)
Talat, Z., Blix, H., Valvoda, J., Ganesh, M. I., Cotterell, R., and Williams, A · 2021
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Ethical-advice taker: Do language models understand natural language interventions?
Zhao, J., Khashabi, D., Khot, T., Sabharwal, A., and Chang, K.-W · 2021
Cited alongside, same era.
Scaling instruction-finetuned language models
Chung, H. W., Hou, L., Longpre, S., Zoph, B., Tay, Y., Fedus, W., Li, E., Wang, X., Dehghani, M., Brahma, S., et al · 2022
Cited alongside, same era.
Decomposed prompting: A modular approach for solving complex tasks
Khot, T., Trivedi, H., Finlayson, M., Fu, Y., Richardson, K., Clark, P., and Sabharwal, A · 2022
Cited alongside, same era.
Large language models are zero-shot reasoners
Kojima, T., Gu, S. S., Reid, M., Matsuo, Y., and Iwasawa, Y · 2022
Beyond the imitation game: Quantifying and extrapolating the capabilities of language models, 2022
Srivastava, A., Rastogi, A., Rao, A., et al · 2022
Later among the works it cites.
Chain-of-thought prompting elicits reasoning in large language models
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Xia, F., Chi, E. H., Le, Q. V., Zhou, D., et al · 2022
Later among the works it cites.
Towards answering open-ended ethical quandary questions
Bang, Y., Lee, N., Yu, T., Khalatbari, L., Xu, Y., Cahyawijaya, S., Su, D., Wilie, B., Barraud, R., Barezi, E. J., et al · 2023
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Let’s verify step by step, 2023
Lightman, H., Kosaraju, V., Burda, Y., Edwards, H., Baker, B., Lee, T., Leike, J., Schulman, J., Sutskever, I., and Cobbe, K · 2023
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Self-consistency improves chain of thought reasoning in language models
Wang, X., Wei, J., Schuurmans, D., Le, Q. V., Chi, E. H., Narang, S., Chowdhery, A., and Zhou, D · 2023
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Cited alongside, same era.
Reframing instructional prompts to GPTk’s language
Mishra, S., Khashabi, D., Baral, C., Choi, Y., and Hajishirzi, H · 2022
Cited alongside, same era.
Is a question decomposition unit all we need?
Patel, P., Mishra, S., Parmar, M., and Baral, C · 2022
Cited alongside, same era.
Measuring and narrowing the compositionality gap in language models
Press, O., Zhang, M., Min, S., Schmidt, L., Smith, N. A., and Lewis, M · 2022
Cited alongside, same era.
Aligning {ai} with shared human values
Hendrycks, D., Burns, C., Basart, S., Critch, A., Li, J., Song, D., and Steinhardt, J
Cited in the paper.
Measuring massive multitask language understanding
Hendrycks, D., Burns, C., Basart, S., Zou, A., Mazeika, M., Song, D., and Steinhardt, J
Cited in the paper.
Yao, S., Yu, D., Zhao, J., Shafran, I., Griffiths, T. L., Cao, Y., and Narasimhan, K · 2023
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Least-to-most prompting enables complex reasoning in large language models, 2023
Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., and Chi, E · 2023
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