Language Model Alignment in Multilingual Trolley Problems
Paper · 2024 · Zhijing Jin, Max Kleiman-Weiner, Giorgio Piatti et al.
Summary
Most moral-alignment benchmarks for LLMs are English-only and reflect mainly US respondents, so it is unclear whether models' moral choices match the preferences of speakers of other languages. The authors address this with MultiTP. They adapt the autonomous-vehicle dilemmas of the Moral Machine experiment, which gathered over 40 million human judgments from more than 200 countries, into LLM prompts and translate them into 107 languages. Each scenario changes one controlled attribute of the two groups at risk, which lets the authors isolate the effect of six dimensions: species, gender, fitness, social status, age and number of lives.
They test 19 open- and closed-weight LLMs and compare the models' preferences with human data mapped to language speaker populations. They also check robustness to paraphrasing and to the order of options. Few models align well with human preferences overall, and alignment varies noticeably between languages. However, low-resource languages are not systematically disadvantaged compared with major ones. The resource supports pluralistic, multilingual AI-ethics evaluation.
Key points
- MultiTP contains 98,440 trolley-problem scenarios in 107 languages and is grounded in the Moral Machine human data (about 40M responses).
- The 19 evaluated models include the GPT-3, GPT-4 and GPT-4o Mini, Llama 2/3/3.1, Gemma 2, Qwen 2, Mistral 7B and Phi-3/3.5 families.
- Swapping the order of the two options rarely changes answers. Consistency ranges from 99.5% (GPT-4o Mini) down to 87.1% (GPT-3), the least stable model.
- Supplementary analyses cover the Global East/West/South regions, Llama-family preference profiles and jailbroken "uncensored" variants. Jailbreaking worked best on Llama 3.1 8B.
- Code and data: github.com/causalNLP/multiTP.
Institutions
Fields, topics and concepts
📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Social Sciences · 1,772 worksSafety Research · 543 worksDecision Sciences · 209 worksNeuroscience · 165 worksCognitive Neuroscience · 164 worksInformation Systems and Management · 41 works
🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 3,320 worksMultilingual & cross-lingual NLP · 828 works
Cite
Cite as Zhijing Jin, Max Kleiman-Weiner, Giorgio Piatti, Sydney Levine, Jiarui Liu, Fernando Gonzalez, Francesco Ortu, Andras Strausz, Mrinmaya Sachan, Rada Mihalcea, Yejin Choi and Bernhard Scholkopf. 2024. Language Model Alignment in Multilingual Trolley Problems. arXiv:2407.02273.
@misc{jin2024language,
title = {{Language Model Alignment in Multilingual Trolley Problems}},
author = {Zhijing Jin, Max Kleiman-Weiner, Giorgio Piatti, Sydney Levine, Jiarui Liu, Fernando Gonzalez, Francesco Ortu, Andras Strausz, Mrinmaya Sachan, Rada Mihalcea, Yejin Choi and Bernhard Scholkopf},
year = {2024},
doi = {10.48550/arxiv.2407.02273},
eprint = {2407.02273},
archivePrefix = {arXiv},
url = {https://doi.org/10.48550/arxiv.2407.02273}
}References
- Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774, 2023.
- Muhammad Farid Adilazuarda, Sagnik Mukherjee, Pradhyumna Lavania, Siddhant Singh, Ashutosh Dwivedi, Alham Fikri Aji, Jacki O’Neill, Ashutosh Modi, and Monojit Choudhury. Towards measuring and modeling" culture" in llms: A survey. arXiv preprint arXiv:2403.15412, 2024.
- Kabir Ahuja, Harshita Diddee, Rishav Hada, Millicent Ochieng, Krithika Ramesh, Prachi Jain, Akshay Nambi, Tanuja Ganu, Sameer Segal, Maxamed Axmed, et al. Mega: Multilingual evaluation of generative ai. arXiv preprint arXiv:2303.12528, 2023.
- Guilherme FCF Almeida, José Luiz Nunes, Neele Engelmann, Alex Wiegmann, and Marcelo de Araújo. Exploring the psychology of gpt-4’s moral and legal reasoning. arXiv preprint arXiv:2308.01264, 2023. find
- Usman Anwar, Abulhair Saparov, Javier Rando, Daniel Paleka, Miles Turpin, Peter Hase, Ekdeep Singh Lubana, Erik Jenner, Stephen Casper, Oliver Sourbut, et al. Foundational challenges in assuring alignment and safety of large language models. arXiv preprint arXiv:2404.09932, 2024. find
- Andy Arditi, Oscar Obeso, Aaquib Syed, Daniel Paleka, Nina Panickssery, Wes Gurnee, and Neel Nanda. Refusal in language models is mediated by a single direction. Advances in Neural Information Processing Systems, 37:136037–136083, 2025.
- Mikel Artetxe, Sebastian Ruder, and Dani Yogatama. On the cross-lingual transferability of monolingual representations. CoRR, abs/1910.11856, 2019. URL http://arxiv.org/abs/1910. 11856. ↗
- Akari Asai, Sneha Kudugunta, Xinyan Velocity Yu, Terra Blevins, Hila Gonen, Machel Reid, Yulia Tsvetkov, Sebastian Ruder, and Hannaneh Hajishirzi. Buffet: Benchmarking large language models for few-shot cross-lingual transfer, 2023.
- Mohammad Atari, Mona J Xue, Peter S Park, Damián Blasi, and Joseph Henrich. Which humans? 2023. find
- Edmond Awad, Sohan Dsouza, Richard Kim, Jonathan Schulz, Joseph Henrich, Azim Shariff, Jean-François Bonnefon, and Iyad Rahwan. The moral machine experiment. Nature, 563(7729):59, 2018. find
- Edmond Awad, Sohan Dsouza, Azim Shariff, Iyad Rahwan, and Jean-François Bonnefon. Universals and variations in moral decisions made in 42 countries by 70,000 participants. Proceedings of the National Academy of Sciences, 117(5):2332–2337, 2020a. find
- Edmond Awad, Sydney Levine, Max Kleiman-Weiner, Sohan Dsouza, Joshua B Tenenbaum, Azim Shariff, Jean-François Bonnefon, and Iyad Rahwan. Drivers are blamed more than their automated cars when both make mistakes. Nature human behaviour, 4(2):134–143, 2020b. find
- Jinze Bai, Shuai Bai, Yunfei Chu, Zeyu Cui, Kai Dang, Xiaodong Deng, Yang Fan, Wenbin Ge, Yu Han, Fei Huang, et al. Qwen technical report. arXiv preprint arXiv:2309.16609, 2023.
- Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy Jones, Anna Chen, Anna Goldie, Azalia Mirhoseini, Cameron McKinnon, Carol Chen, Catherine Olsson, Christopher Olah, Danny Hernandez, Dawn Drain, Deep Ganguli, Dustin Li, Eli Tran-Johnson, Ethan Perez, Jamie Kerr, Jared… — Constitutional AI: Harmlessness from AI Feedback
- Yoshua Bengio, Geoffrey Hinton, Andrew Yao, Dawn Song, Pieter Abbeel, Yuval Noah Harari, Ya-Qin Zhang, Lan Xue, Shai Shalev-Shwartz, Gillian Hadfield, et al. Managing ai risks in an era of rapid progress. arXiv preprint arXiv:2310.17688, 2023. find
- Rishi Bommasani, Drew A Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, et al. On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258, 2021.
- Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter,… — Language Models are Few-Shot Learners
- Stijn Bruers and Johan Braeckman. Philosophia, 42:251–269, 2014. find
- A review and systematization of the trolley problem. Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott M. Lundberg, Harsha Nori, Hamid Palangi, Marco Túlio Ribeiro, and Yi Zhang. Sparks of artificial general intel… — Sparks of Artificial General Intelligence: Early experiments with GPT-4
- Samuel Cahyawijaya, Holy Lovenia, Tiezheng Yu, Willy Chung, and Pascale Fung. Instructalign: High-and-low resource language alignment via continual crosslingual instruction tuning. arXiv preprint arXiv:2305.13627, 2023.
- Samuel Cahyawijaya, Delong Chen, Yejin Bang, Leila Khalatbari, Bryan Wilie, Ziwei Ji, Etsuko Ishii, and Pascale Fung. High-dimension human value representation in large language models. arXiv preprint arXiv:2404.07900, 2024. find
- Nicholas Carlini, Milad Nasr, Christopher A. Choquette-Choo, Matthew Jagielski, Irena Gao, Pang Wei Koh, Daphne Ippolito, Florian Tramèr, and Ludwig Schmidt. Are aligned neural networks adversarially aligned? In Alice Oh, Tristan Naumann, Amir Globerson, Kate Saenko, Moritz Hardt, and Sergey Levine… find
- Wei-Lin Chiang, Lianmin Zheng, Ying Sheng, Anastasios Nikolas Angelopoulos, Tianle Li, Dacheng Li, Banghua Zhu, Hao Zhang, Michael I. Jordan, Joseph E. Gonzalez, and Ion Stoica. Chatbot arena: An open platform for evaluating llms by human preference. In Forty-first International Conference on Machin…
- OpenReview.net, 2024. URL https://openreview.net/forum?id=3MW8GKNyzI. ↗
- Alexis Conneau, Ruty Rinott, Guillaume Lample, Adina Williams, Samuel R. Bowman, Holger Schwenk, and Veselin Stoyanov. XNLI: evaluating cross-lingual sentence representations. In Ellen Riloff, David Chiang, Julia Hockenmaier, and Jun’ichi Tsujii (eds.), Proceedings of the 2018 find
- Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, October 31 - November 4, 2018, pp. 2475–2485. Association for Computational Linguistics, 2018. doi: 10.18653/v1/d18-1269. URL https://doi.org/10.18653/v1/d18-1269. ↗
- Marta R Costa-jussà, James Cross, Onur Çelebi, Maha Elbayad, Kenneth Heafield, Kevin Heffernan, Elahe Kalbassi, Janice Lam, Daniel Licht, Jean Maillard, et al. No language left behind: Scaling human-centered machine translation. arXiv preprint arXiv:2207.04672, 2022. find
- Danica Dillion, Niket Tandon, Yuling Gu, and Kurt Gray. Can ai language models replace human participants? Trends in Cognitive Sciences, 2023. find
- Jonathan Dunn, Benjamin Adams, and Harish Tayyar Madabushi. Pre-trained language models represent some geographic populations better than others. arXiv preprint arXiv:2403.11025, 2024. find
- Esin Durmus, Karina Nguyen, Thomas I Liao, Nicholas Schiefer, Amanda Askell, Anton Bakhtin, Carol Chen, Zac Hatfield-Dodds, Danny Hernandez, Nicholas Joseph, et al. Towards measuring the representation of subjective global opinions in language models. arXiv preprint arXiv:2306.16388, 2023.
- Kawin Ethayarajh and Dan Jurafsky. The authenticity gap in human evaluation. In Yoav Goldberg, Zornitsa Kozareva, and Yue Zhang (eds.), Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pp. 6056–6070, Abu Dhabi, United Arab Emirates, December 2022. Association f… find
- Philippa Foot. The problem of abortion and the doctrine of double effect, volume 5. Oxford, 1967. find
- Maxwell Forbes, Jena D Hwang, Vered Shwartz, Maarten Sap, and Yejin Choi. Social chemistry 101: Learning to reason about social and moral norms. In EMNLP, 2020. URL https://www.aclweb.org/anthology/2020.emnlp-main.48. ↗
- Jan-Philipp Fränken, Kanishk Gandhi, Tori Qiu, Ayesha Khawaja, Noah D Goodman, and Tobias Gerstenberg. Procedural dilemma generation for evaluating moral reasoning in humans and language models. arXiv preprint arXiv:2404.10975, 2024. — [arXiv:2404.10975](https://arxiv.org/abs/2404.10975) find
- Kathleen C Fraser, Svetlana Kiritchenko, and Esma Balkir. Does moral code have a moral code? probing delphi’s moral philosophy. arXiv preprint arXiv:2205.12771, 2022. find
- Mitchell L Gordon, Michelle S Lam, Joon Sung Park, Kayur Patel, Jeff Hancock, Tatsunori Hashimoto, and Michael S Bernstein. Jury learning: Integrating dissenting voices into machine learning models. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems, pp. 1–19, 2022. find
- Shinsuke Goto, Donghui Lin, and Toru Ishida. Crowdsourcing for evaluating machine translation quality. In Nicoletta Calzolari, Khalid Choukri, Thierry Declerck, Hrafn Loftsson, Bente Maegaard, Joseph Mariani, Asuncion Moreno, Jan Odijk, and Stelios Piperidis (eds.), Proceedings of the Ninth Internat… find
- Naman Goyal, Cynthia Gao, Vishrav Chaudhary, Peng-Jen Chen, Guillaume Wenzek, Da Ju, Sanjana Krishnan, Marc’Aurelio Ranzato, Francisco Guzmán, and Angela Fan. The FLORES-101 evaluation benchmark for low-resource and multilingual machine translation. CoRR, abs/2106.03193, 2021. URL https://arxiv.org/… ↗
- Dan Hendrycks, Collin Burns, Steven Basart, Andrew Critch, Jerry Li, Dawn Song, and Jacob Steinhardt. Aligning AI with shared human values. In International Conference on Learning Representations, 2021a. URL https://openreview.net/forum?id=dNy\\_RKzJacY. ↗
- Dan Hendrycks, Nicholas Carlini, John Schulman, and Jacob Steinhardt. Unsolved problems in ML safety. CoRR, abs/2109.13916, 2021b. URL https://arxiv.org/abs/2109.13916. ↗
- Joseph Henrich, Steven J Heine, and Ara Norenzayan. The weirdest people in the world? Behavioral and brain sciences, 33(2-3):61–83, 2010. find
- Carolin Holtermann, Paul Röttger, Timm Dill, and Anne Lauscher. Evaluating the elementary multilingual capabilities of large language models with multiq, 2024. find
- Albert Q Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, et al. Mistral 7b. arXiv preprint arXiv:2310.06825, 2023. find
- Albert Q Jiang, Alexandre Sablayrolles, Antoine Roux, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, et al. Mixtral of experts. arXiv preprint arXiv:2401.04088, 2024. find
- Liwei Jiang, Jena D Hwang, Chandra Bhagavatula, Ronan Le Bras, Maxwell Forbes, Jon Borchardt, Jenny Liang, Oren Etzioni, Maarten Sap, and Yejin Choi. Delphi: Towards machine ethics and norms. arXiv preprint arXiv:2110.07574, 2021a. find
- Liwei Jiang, Jena D Hwang, Chandra Bhagavatula, Ronan Le Bras, Jenny Liang, Jesse Dodge, Keisuke Sakaguchi, Maxwell Forbes, Jon Borchardt, Saadia Gabriel, et al. Can machines learn morality? the delphi experiment. arXiv preprint arXiv:2110.07574, 2021b. find
- Wenying Jiang. The relationship between culture and language. ELT journal, 54(4):328–334, 2000. find
- Wenxiang Jiao, Wenxuan Wang, Jen-tse Huang, Xing Wang, Shuming Shi, and Zhaopeng Tu. Is chatgpt a good translator? yes with gpt-4 as the engine. arXiv preprint arXiv:2301.08745, 2023.
- Zhijing Jin, Sydney Levine, Fernando Gonzalez Adauto, Ojasv Kamal, Maarten Sap, Mrinmaya Sachan, Rada Mihalcea, Josh Tenenbaum, and Bernhard Schölkopf. When to make exceptions: Exploring language models as accounts of human moral judgment. Advances in neural information processing systems, 35:28458–… find
- Zhijing Jin, Nils Heil, Jiarui Liu, Shehzaad Dhuliawala, Yahang Qi, Bernhard Schölkopf, Rada Mihalcea, and Mrinmaya Sachan. Implicit personalization in language models: A systematic study, 2024. find
- Richard Kim, Max Kleiman-Weiner, Andrés Abeliuk, Edmond Awad, Sohan Dsouza, Joshua B Tenenbaum, and Iyad Rahwan. A computational model of commonsense moral decision making. In Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society, pp. 197–203, 2018. find
- Hannah Rose Kirk, Alexander Whitefield, Paul Röttger, Andrew Bean, Katerina Margatina, Juan Ciro, Rafael Mosquera, Max Bartolo, Adina Williams, He He, et al. The prism alignment project: What participatory, representative and individualised human feedback reveals about the subjective and multicultur… find
- Max Kleiman-Weiner, Tobias Gerstenberg, Sydney Levine, and Joshua B Tenenbaum. Inference of intention and permissibility in moral decision making. In CogSci. Citeseer, 2015. find
- Max Kleiman-Weiner, Rebecca Saxe, and Joshua B Tenenbaum. Learning a commonsense moral theory. Cognition, 167:107–123, 2017. find
- Sebastian Krügel, Andreas Ostermaier, and Matthias W. Uhl. Chatgpt’s inconsistent moral advice influences users’ judgment. Scientific Reports, 13, 2023. URL https://api.semanticscholar.org/CorpusID:257986789. ↗
- JR Landis. The measurement of observer agreement for categorical data. Biometrics, 1977. find
- Alon Lavie. Evaluating the output of machine translation systems. In Proceedings of Machine Translation Summit XIII: Tutorial Abstracts, Xiamen, China, September 19 2011. URL https://aclanthology.org/2011.mtsummit-tutorials.3. ↗
- Nelson F Liu, Kevin Lin, John Hewitt, Ashwin Paranjape, Michele Bevilacqua, Fabio Petroni, and Percy Liang. Lost in the middle: How language models use long contexts. Transactions of the Association for Computational Linguistics, 12:157–173, 2024.
- Shayne Longpre, Yi Lu, and Joachim Daiber. MKQA: A linguistically diverse benchmark for multilingual open domain question answering. Trans. Assoc. Comput. Linguistics, 9:1389–1406, 2021. doi: 10.1162/tacl\\_a\\_00433. URL https://doi.org/10.1162/tacl_a_00433. ↗
- J Macqueen. Some methods for classification and analysis of multivariate observations. In Proceedings of 5-th Berkeley Symposium on Mathematical Statistics and Probability/University of California Press, 1967. find
- Rohin Manvi, Samar Khanna, Marshall Burke, David Lobell, and Stefano Ermon. Large language models are geographically biased. arXiv preprint arXiv:2402.02680, 2024. find
- Meta. Introducing meta llama 3: The most capable openly available llm to date, 2024. URL https://ai.meta.com/blog/meta-llama-3/. ↗
- John Mikhail. Universal moral grammar: Theory, evidence and the future. Trends in cognitive sciences, 11(4):143–152, 2007. find
- Xuan-Phi Nguyen, Mahani Aljunied, Shafiq Joty, and Lidong Bing. Democratizing LLMs for low-resource languages by leveraging their English dominant abilities with linguistically-diverse prompts. In Lun-Wei Ku, Andre Martins, and Vivek Srikumar (eds.), Proceedings of the 62nd Annual Meeting of the Ass…
- Allen Nie, Yuhui Zhang, Atharva Amdekar, Chris Piech, Tatsunori B. Hashimoto, and Tobias Gerstenberg. Moca: Measuring human-language model alignment on causal and moral judgment tasks. In Alice Oh, Tristan Naumann, Amir Globerson, Kate Saenko, Moritz Hardt, and Sergey Levine (eds.), Advances in Neur…
- Keqin Peng, Liang Ding, Qihuang Zhong, Li Shen, Xuebo Liu, Min Zhang, Yuanxin Ouyang, and Dacheng Tao. Towards making the most of chatgpt for machine translation. arXiv preprint arXiv:2303.13780, 2023.
- Abhinav Rao, Akhila Yerukola, Vishwa Shah, Katharina Reinecke, and Maarten Sap. Normad: A benchmark for measuring the cultural adaptability of large language models. arXiv preprint arXiv:2404.12464, 2024. — [arXiv:2404.12464](https://arxiv.org/abs/2404.12464) find
- Michael J Ryan, William Held, and Diyi Yang. Unintended impacts of llm alignment on global representation. arXiv preprint arXiv:2402.15018, 2024.
- Shibani Santurkar, Esin Durmus, Faisal Ladhak, Cinoo Lee, Percy Liang, and Tatsunori Hashimoto. Whose opinions do language models reflect? arXiv preprint arXiv:2303.17548, 2023.
- Maarten Sap, Hannah Rashkin, Derek Chen, Ronan Le Bras, and Yejin Choi. Social iqa: Commonsense reasoning about social interactions. In EMNLP 2019, 2019. find
- Chelsea Schein. The importance of context in moral judgments. Perspectives on Psychological Science, 15(2):207–215, 2020. find
- Nino Scherrer, Claudia Shi, Amir Feder, and David Blei. Evaluating the moral beliefs encoded in llms. Advances in Neural Information Processing Systems, 36, 2024. find
- Patrick Schramowski, Cigdem Turan, Nico Andersen, Constantin A Rothkopf, and Kristian Kersting. Large pre-trained language models contain human-like biases of what is right and wrong to do. Nature Machine Intelligence, 4(3):258–268, 2022. URL https://doi.org/10.1038/ s42256-022-00458-8. find
- Siqi Shen, Lajanugen Logeswaran, Moontae Lee, Honglak Lee, Soujanya Poria, and Rada Mihalcea. Understanding the capabilities and limitations of large language models for cultural commonsense. In Proceedings of the North American Association for Computational Linguistics, 2024. find
- Weiyan Shi, Ryan Li, Yutong Zhang, Caleb Ziems, Raya Horesh, Rogério Abreu de Paula, Diyi Yang, et al. Culturebank: An online community-driven knowledge base towards culturally aware language technologies. arXiv preprint arXiv:2404.15238, 2024.
- Taylor Sorensen, Liwei Jiang, Jena D. Hwang, Sydney Levine, Valentina Pyatkin, Peter West, Nouha Dziri, Ximing Lu, Kavel Rao, Chandra Bhagavatula, Maarten Sap, John Tasioulas, and Yejin Choi. Value kaleidoscope: Engaging ai with pluralistic human values, rights, and duties. Proceedings of the AAAI C…
- Taylor Sorensen, Liwei Jiang, Jena D Hwang, Sydney Levine, Valentina Pyatkin, Peter West, Nouha Dziri, Ximing Lu, Kavel Rao, Chandra Bhagavatula, et al. Value kaleidoscope: Engaging ai with pluralistic human values, rights, and duties. In Proceedings of the AAAI Conference on Artificial Intelligence…
- Taylor Sorensen, Jared Moore, Jillian Fisher, Mitchell Gordon, Niloofar Mireshghallah, Christopher Michael Rytting, Andre Ye, Liwei Jiang, Ximing Lu, Nouha Dziri, et al. A roadmap to pluralistic alignment. arXiv preprint arXiv:2402.05070, 2024c.
- Marc Steen. The problem with the trolley problem and the need for systems thinking. Communications of the ACM, 67(6):30–32, 2024. find
- Kazuhiro Takemoto. The moral machine experiment on large language models. Royal Society open science, 11(2):231393, 2024. find
- Eshaan Tanwar, Subhabrata Dutta, Manish Borthakur, and Tanmoy Chakraborty. Multilingual LLMs are better cross-lingual in-context learners with alignment. In Anna Rogers, Jordan Boyd-Graber, and Naoaki Okazaki (eds.), Proceedings of the 61st Annual Meeting of the Association for Computational Linguis…
- Judith Jarvis Thomson. The trolley problem. Yale LJ, 94:1395, 1984. find
- Robert L. Thorndike. Who belongs in the family? Psychometrika, 18:267–276, 1953. URL https://api.semanticscholar.org/CorpusID:120467216. ↗
- Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurélien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. LLaMA: Open and efficient foundation language models. CoRR, abs/2302…
- Alexander Wei, Nika Haghtalab, and Jacob Steinhardt. Jailbroken: How does LLM safety training fail? In Alice Oh, Tristan Naumann, Amir Globerson, Kate Saenko, Moritz Hardt, and Sergey Levine (eds.), Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Process…
- Wikipedia. List of languages by total number of speakers. https://en.wikipedia.org/wiki/ List_of_languages_by_total_number_of_speakers, 2024. URL https://en.wikipedia. org/wiki/List_of_languages_by_total_number_of_speakers. [Accessed: 2024-10-02]. ↗
- Alex Young, Bei Chen, Chao Li, Chengen Huang, Ge Zhang, Guanwei Zhang, Heng Li, Jiangcheng Zhu, Jianqun Chen, Jing Chang, Kaidong Yu, Peng Liu, Qiang Liu, Shawn Yue, Senbin Yang, Shiming Yang, Tao Yu, Wen Xie, Wenhao Huang, Xiaohui Hu, Xiaoyi Ren, Xinyao Niu, Pengcheng Nie, Yuchi Xu, Yudong Liu, Yue… — Yi: Open Foundation Models by 01.AI
- Yiran Zhao, Wenxuan Zhang, Guizhen Chen, Kenji Kawaguchi, and Lidong Bing. How do large language models handle multilingualism? arXiv preprint arXiv:2402.18815, 2024.
- Wenhao Zhu, Hongyi Liu, Qingxiu Dong, Jingjing Xu, Shujian Huang, Lingpeng Kong, Jiajun Chen, and Lei Li. Multilingual machine translation with large language models: Empirical results and analysis. arXiv preprint arXiv:2304.04675, 2023.
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