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LaMDA: Language Models for Dialog Applications

Written for the Research library (claude-opus-5-5) from the paper text. Original — arxiv.org/pdf/2201.08239

Summary

LaMDA is a family of Transformer-based dialog models from Google, ranging from 2B to 137B parameters. The paper tackles a known weakness of large conversational models: they become more fluent as they grow, but scale does little to make them safer or more factually reliable. The models were pre-trained on 1.56T words of public dialog and web documents. Crowdworkers then annotated responses for three goals: quality (sensibleness, specificity, interestingness), safety against an illustrative set of human values, and groundedness in external sources.

The same model is fine-tuned to act as a generator and as classifiers. In this role it removes unsafe candidates and re-ranks the rest. It also learns to call a toolset (information retrieval, translator, calculator), imitating how annotators researched factual claims.

Scaling by itself improved quality but left safety and groundedness well below human levels. Fine-tuning produced clear gains on every metric and brought quality close to crowdworker performance. The work shows that modest annotated data plus tool use can move dialog systems toward safer, verifiable answers.

Key points

  • Model sizes span 2B–137B parameters, with pre-training on 1.56T words of dialog and web text.
  • Evaluation relies on human judgments rather than automatic metrics like BLEU or perplexity. It covers SSI (sensibleness, specificity, interestingness), safety, and groundedness.
  • Groundedness comes from a toolset of an IR system, a translator and a calculator. The model is trained to mimic crowdworkers who used these tools to check facts.
  • The authors tested LaMDA in education and content recommendation by preconditioning it on a few dialog turns. Over four in five responses stayed in role, and fine-tuned LaMDA was significantly more helpful than the pre-trained-only model.
  • The largest model trained on 1024 TPU-v3 chips for 57.7 days (≈3.55E+23 FLOPs, ≈451 MWh). Total training emissions were ≈26 tCO2e, about 21.2× lower than GPT-3.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Other

Cited by

642 papers of our library cite it (of the 140673 whose bibliographies we read); 90 of them have a page here, 552 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2201.08239 · arXiv 2201.08239

@misc{thoppilan2022lamda,
  title = {{LaMDA: Language Models for Dialog Applications}},
  author = {Thoppilan, Romal and Freitas, Daniel De and Hall, Jamie and Shazeer, Noam and Kulshreshtha, Apoorv and Cheng, Heng-Tze and Jin, Alicia and Bos, Taylor and Baker, Leslie and Du, Yu and Li, YaGuang and Lee, Hongrae and Zheng, Huaixiu Steven and Ghafouri, Amin and Menegali, Marcelo and Huang, Yanping and Krikun, Maxim and Lepikhin, Dmitry and Qin, James and Chen, Dehao and Xu, Yuanzhong and Chen, Zhifeng and Roberts, Adam and Bosma, Maarten and Zhao, Vincent and Zhou, Yanqi and Chang, Chung-Ching and Krivokon, Igor and Rusch, Will and Pickett, Marc and Srinivasan, Pranesh and Man, Laichee and Meier-Hellstern, Kathleen and Morris, Meredith Ringel and Doshi, Tulsee and Santos, Renelito Delos and Duke, Toju and Soraker, Johnny and Zevenbergen, Ben and Prabhakaran, Vinodkumar and Diaz, Mark and Hutchinson, Ben and Olson, Kristen and Molina, Alejandra and Hoffman-John, Erin and Lee, Josh and Aroyo, Lora and Rajakumar, Ravi and Butryna, Alena and Lamm, Matthew and Kuzmina, Viktoriya and Fenton, Joe and Cohen, Aaron and Bernstein, Rachel and Kurzweil, Ray and Aguera-Arcas, Blaise and Cui, Claire and Croak, Marian and Chi, Ed and Le, Quoc},
  year = {2022},
  doi = {10.48550/arxiv.2201.08239},
  eprint = {2201.08239},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2201.08239}
}

References

109 references, 40 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Ryan Kiros, Yukun Zhu, Ruslan R Salakhutdinov, Richard Zemel, Raquel Urtasun, Antonio Torralba, and Sanja Fidler. Skip-thought vectors. In Advances in Neural Information Processing Systems, pages 3294–3302, 2015. find
  2. Andrew M Dai and Quoc V Le. Semi-supervised sequence learning. In Advances in Neural Information Processing Systems, 2015. find
  3. Matthew Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. Deep contextualized word representations. In NAACL, 2018. find
  4. Jeremy Howard and Sebastian Ruder. Universal language model fine-tuning for text classification. In ACL, 2018. find
  5. Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. Improving language understanding by generative pre-training. https://blog.openai.com/language-unsupervised, 2018. ↗
  6. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. BERT: Pre-training of deep bidirectional transformers for language understanding. In NAACL-HLT, 2019. find
  7. Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, and Quoc V Le. XLNet: Generalized autoregressive pretraining for language understanding. In NeurIPS, 2019. find our card
  8. Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut. Albert: A lite bert for self-supervised learning of language representations. In International Conference on Learning Representations, 2020. URL https://openreview.net/forum?id=H1eA7AEtvS. ↗
  9. Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. RoBERTa: A robustly optimized BERT pretraining approach. arXiv preprint arXiv:1907.11692, 2019. find
  10. Kevin Clark, Minh-Thang Luong, Quoc V. Le, and Christopher D. Manning. ELECTRA: Pre-training text encoders as discriminators rather than generators. In ICLR, 2020. find
  11. Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of Machine Learning Research, 2020. find
  12. 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,… find
  13. Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B. Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei. Scaling laws for neural language models. arXiv preprint arXiv:2001.08361, 2020. find
  14. Lifeng Shang, Zhengdong Lu, and Hang Li. Neural responding machine for short-text conversation. In ACL, 2015. find our card
  15. Alessandro Sordoni, Michel Galley, Michael Auli, Chris Brockett, Yangfeng Ji, Margaret Mitchell, Jian-Yun Nie, Jianfeng Gao, and Bill Dolan. A neural network approach to context-sensitive generation of conversational responses. arXiv preprint arXiv:1506.06714, 2015. find our card
  16. Oriol Vinyals and Quoc V. Le. A neural conversational model. In ICML Workshop, 2015. find our card
  17. Daniel Adiwardana, Minh-Thang Luong, David R. So, Jamie Hall, Noah Fiedel, Romal Thoppilan, Zi Yang, Apoorv Kulshreshtha, Gaurav Nemade, Yifeng Lu, and Quoc V. Le. Towards a human-like open-domain chatbot. arXiv preprint arXiv:2001.09977, 2020. find our card
  18. Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, and Jason Weston. Recipes for building an open-domain chatbot. arXiv preprint arXiv:2004.13637, 2020. find our card
  19. Tomas Mikolov, Martin Karafiát, Lukas Burget, Jan Cernockỳ, and Sanjeev Khudanpur. Recurrent neural network based language model. In INTERSPEECH, 2010. find
  20. Ilya Sutskever, James Martens, and Geoffrey E Hinton. Generating text with recurrent neural networks. In ICML, 2011. find
  21. Rafal Józefowicz, Oriol Vinyals, Mike Schuster, Noam Shazeer, and Yonghui Wu. Exploring the limits of language modeling. arXiv preprint arXiv:1602.02410, 2016. find
  22. Jeremy Howard and Sebastian Ruder. Universal language model fine-tuning for text classification. In ACL, 2018. find
  23. Alec Radford, Luke Metz, and Soumith Chintala. Unsupervised representation learning with deep convolutional generative adversarial networks. In ICLR, 2016. find our card
  24. Jack W. Rae, Sebastian Borgeaud, Trevor Cai, Katie Millican, Jordan Hoffmann, Francis Song, John Aslanides, Sarah Henderson, Roman Ring, Susannah Young, Eliza Rutherford, Tom Hennigan, Jacob Menick, Albin Cassirer, Richard Powell, George van den Driessche, Lisa Anne Hendricks, Maribeth Rauh, Po-Sen… find
  25. Emily Dinan, Varvara Logacheva, Valentin Malykh, Alexander H. Miller, Kurt Shuster, Jack Urbanek, Douwe Kiela, Arthur Szlam, Iulian Serban, Ryan Lowe, Shrimai Prabhumoye, Alan W. Black, Alexander I. Rudnicky, Jason Williams, Joelle Pineau, Mikhail S. Burtsev, and Jason Weston. The second conversatio… find our card
  26. Saizheng Zhang, Emily Dinan, Jack Urbanek, Arthur Szlam, Douwe Kiela, and Jason Weston. Personalizing dialogue agents: I have a dog, do you have pets too? ACL, 2018. find our card
  27. Jiwei Li, Michel Galley, Chris Brockett, Jianfeng Gao, and Bill Dolan. A diversity-promoting objective function for neural conversation models. arXiv preprint arXiv:1510.03055, 2015. find our card
  28. Iulian Vlad Serban, Ryan Lowe, Laurent Charlin, and Joelle Pineau. Generative deep neural networks for dialogue: A short review. arXiv preprint arXiv:1611.06216, 2016. — [arXiv:1611.06216](https://arxiv.org/abs/1611.06216) find
  29. Thomas Wolf, Victor Sanh, Julien Chaumond, and Clement Delangue. Transfertransfo: A transfer learning approach for neural network based conversational agents. In NeurIPS Workshop on Conversational AI, 2019. find
  30. Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, and Bill Dolan. Dialogpt: Large-scale generative pre-training for conversational response generation. arXiv preprint arXiv:1911.00536, 2019. find our card
  31. Kurt Shuster, Spencer Poff, Moya Chen, Douwe Kiela, and Jason Weston. Retrieval augmentation reduces hallucination in conversation. arXiv preprint arXiv:2104.07567, 2021. find our card
  32. Adam Roberts, Colin Raffel, and Noam Shazeer. How much knowledge can you pack into the parameters of a language model? In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, pages 5418–5426, November 2020. find
  33. Nan Du, Yanping Huang, Andrew M. Dai, Simon Tong, Dmitry Lepikhin, Yuanzhong Xu, Maxim Krikun, Yanqi Zhou, Adams Wei Yu, Orhan Firat, Barret Zoph, Liam Fedus, Maarten Bosma, Zongwei Zhou, Tao Wang, Yu Emma Wang, Kellie Webster, Marie Pellat, Kevin Robinson, Kathy Meier-Hellstern, Toju Duke, Lucas Di… find our card
  34. Urvashi Khandelwal, Omer Levy, Dan Jurafsk, Luke Zettlemoyer, and Mike Lewis. Generalization through memorization: Nearest neighbor language models. arXiv preprint arXiv:1911.00172, 2019. find
  35. Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen tau Yih, Tim Rocktäschel, Sebastian Riedel, and Douwe Kiela. Retrieval-augmented generation for knowledge-intensive nlp tasks. NeurIPS, 2020. find
  36. Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, and Ming-Wei Chang. Realm: Retrieval-augmented language model pre-training. arXiv preprint arXiv:2002.08909, 2020. find
  37. Gautier Izacard and Edouard Grave. Leveraging passage retrieval with generative models for open domain question answering. arXiv preprint arXiv:2007.01282, 2021. find our card
  38. Fengbin Zhu, Wenqiang Lei, Chao Wang, Jianming Zheng, Soujanya Poria, and Tat-Seng Chua. Retrieving and reading: A comprehensive survey on open-domain question answering. arXiv preprint arXiv:2101.00774, 2021. — [arXiv:2101.00774](https://arxiv.org/abs/2101.00774) find
  39. Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen tau Yih. Dense passage retrieval for open-domain question answering. arXiv preprint arXiv:2004.04906, 2020. find our card
  40. Oleg Lesota, Navid Rekabsaz, Daniel Cohen, Klaus Antonius Grasserbauer, Carsten Eickhoff, and Markus Schedl. A modern perspective on query likelihood with deep generative retrieval models. arXiv preprint arXiv:2106.13618, 2021. — [arXiv:2106.13618](https://arxiv.org/abs/2106.13618) find
  41. Sebastian Borgeaud, Arthur Mensch, Jordan Hoffmann, Trevor Cai, Eliza Rutherford, Katie Millican, George van den Driessche, Jean-Baptiste Lespiau, Bogdan Damoc, Aidan Clark, Diego de Las Casas, Aurelia Guy, Jacob Menick, Roman Ring, Tom Hennigan, Saffron Huang, Loren Maggiore, Chris Jones, Albin Cas… find
  42. Bill Byrne, Karthik Krishnamoorthi, Saravanan Ganesh, and Mihir Sanjay Kale. Tickettalk: Toward human-level performance with end-to-end, transaction-based dialog systems. arXiv preprint arXiv:2012.12458, 2020. find
  43. Leonard Adolphs, Kurt Shuster, Jack Urbanek, Arthur Szlam, and Jason Weston. Reason first, then respond: Modular generation for knowledge-infused dialogue. arXiv preprint arXiv:2111.05204, 2021. find
  44. Reiichiro Nakano, Jacob Hilton, Suchir Balaji, Jeff Wu, Long Ouyang, Christina Kim, Christopher Hesse, Shantanu Jain, Vineet Kosaraju, William Saunders, Xu Jiang, Karl Cobbe, Tyna Eloundou, Gretchen Krueger, Kevin Button, Matthew Knight, Benjamin Chess, and John Schulman. Webgpt: Browser-assisted qu… find our card
  45. Mojtaba Komeili, Kurt Shuster, and Jason Weston. Internet-augmented dialogue generation. arXiv preprint arXiv:2107.07566, 2021. find our card
  46. Shikib Mehri and Maxine Eskenazi. Usr: An unsupervised and reference free evaluation metric for dialog generation. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 681–707, 2020. find our card
  47. Kishore Papineni, Salim Roukos, Todd Ward, and Wei jing Zhu. BLEU: a method for automatic evaluation of machine translation. In ACL, 2002. find our card
  48. Chia-Wei Liu, Ryan Lowe, Iulian Serban, Mike Noseworthy, Laurent Charlin, and Joelle Pineau. How NOT to evaluate your dialogue system: An empirical study of unsupervised evaluation metrics for dialogue response generation. In Proceedings of the 2016 Conference on Empirical Methods in Natural Languag… find
  49. Abigail See, Stephen Roller, Douwe Kiela, and Jason Weston. What makes a good conversation? how controllable attributes affect human judgments. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1… find our card
  50. Margaret Li, Jason Weston, and Stephen Roller. Acute-eval: Improved dialogue evaluation with optimized questions and multi-turn comparisons. In NeurIPS workshop on Conversational AI, 2019. find
  51. Rostislav Nedelchev, Jens Lehmann, and Ricardo Usbeck. Treating dialogue quality evaluation as an anomaly detection problem. In Proceedings of the 12th Conference on Language Resources and Evaluation, pages 508–512, 2020. find
  52. Anu Venkatesh, Chandra Khatri, Ashwin Ram, Fenfei Guo, Raefer Gabriel, Ashish Nagar, Rohit Prasad, Ming Cheng, Behnam Hedayatnia, Angeliki Metallinou, Rahul Goel, Shaohua Yang, and Anirudh Raju. On evaluating and comparing conversational agents. NeurIPS, 2017. find
  53. Emily Dinan, Gavin Abercrombie, A. Stevie Bergman, Shannon Spruit, Dirk Hovy, Y-Lan Boureau, and Verena Rieser. Anticipating safety issues in e2e conversational ai: Framework and tooling. arXiv preprint arXiv:2107.03451, 2021. — [arXiv:2107.03451](https://arxiv.org/abs/2107.03451) find
  54. Laura Weidinger, John Mellor, Maribeth Rauh, Conor Griffin, Jonathan Uesato, Po-Sen Huang, Myra Cheng, Mia Glaese, Borja Balle, Atoosa Kasirzadeh, Zac Kenton, Sasha Brown, Will Hawkins, Tom Stepleton, Courtney Biles, Abeba Birhane, Julia Haas, Laura Rimell, Lisa Anne Hendricks, William Isaac, Sean L… find our card
  55. Samuel Rota Bulò, Lorenzo Porzi, and Peter Kontschieder. Dropout distillation. In ICLR, 2016. find
  56. Kris McGuffie and Alex Newhouse. The radicalization risks of GPT-3 and advanced neural language models. arXiv preprint arXiv:2009.06807, 2020. — [arXiv:2009.06807](https://arxiv.org/abs/2009.06807) find
  57. Abubakar Abid, Maheen Farooqi, and James Zou. Persistent anti-muslim bias in large language models. arXiv preprint arXiv:2101.05783, 2021. find our card
  58. Tolga Bolukbasi, Kai-Wei Chang, James Zou, Venkatesh Saligrama, and Adam Kalai. Man is to computer programmer as woman is to homemaker? debiasing word embeddings. In Advances in Neural Information Processing Systems, 2016. find our card
  59. Christine Basta, Marta R. Costa-jussà, and Noe Casas. Evaluating the underlying gender bias in contextualized word embeddings. In Proceedings of the First Workshop on Gender Bias in Natural Language Processing, August 2019. find
  60. Keita Kurita, Nidhi Vyas, Ayush Pareek, Alan W Black, and Yulia Tsvetkov. Measuring bias in contextualized word representations. In Proceedings of the First Workshop on Gender Bias in Natural Language Processing, August 2019. find our card
  61. Haoran Zhang, Amy X. Lu, Mohamed Abdalla, Matthew McDermott, and Marzyeh Ghassemi. Hurtful words: Quantifying biases in clinical contextual word embeddings. In Proceedings of the ACM Conference on Health, Inference, and Learning, 2020. find
  62. Emily Sheng, Kai-Wei Chang, Premkumar Natarajan, and Nanyun Peng. The woman worked as a babysitter: On biases in language generation. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (E… find our card
  63. Jieyu Zhao, Tianlu Wang, Mark Yatskar, Ryan Cotterell, Vicente Ordonez, and Kai-Wei Chang. Gender bias in contextualized word embeddings. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long… find our card
  64. Wei Guo and Aylin Caliskan. Detecting emergent intersectional biases: Contextualized word embeddings contain a distribution of human-like biases. arXiv preprint arXiv:2006.03955, 2020. — [arXiv:2006.03955](https://arxiv.org/abs/2006.03955) find
  65. Vinodkumar Prabhakaran, Ben Hutchinson, and Margaret Mitchell. Perturbation sensitivity analysis to detect unintended model biases. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2019. find
  66. Chandler May, Alex Wang, Shikha Bordia, Samuel R. Bowman, and Rachel Rudinger. On measuring social biases in sentence encoders. arXiv preprint arXiv:1903.10561, 2019. find our card
  67. Maarten Sap, Dallas Card, Saadia Gabriel, Yejin Choi, and Noah A. Smith. The risk of racial bias in hate speech detection. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 2019. find our card
  68. Shikha Bordia and Samuel R. Bowman. Identifying and reducing gender bias in word-level language models. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop, 2019. find our card
  69. Emily M Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell. On the dangers of stochastic parrots: Can language models be too big? In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 2021. find
  70. Maarten Sap, Saadia Gabriel, Lianhui Qin, Dan Jurafsky, Noah A. Smith, and Yejin Choi. Social bias frames: Reasoning about social and power implications of language. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020. find our card
  71. Ben Hutchinson, Vinodkumar Prabhakaran, Emily Denton, Kellie Webster, Yu Zhong, and Stephen Denuyl. Social biases in NLP models as barriers for persons with disabilities. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020. find our card
  72. Abubakar Abid, Maheen Farooqi, and James Zou. Large language models associate muslims with violence. Nature Machine Intelligence, 2021. find
  73. Nicholas Carlini, Florian Tramer, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom Brown, Dawn Song, Ulfar Erlingsson, Alina Oprea, and Colin Raffel. Extracting training data from large language models. arXiv preprint arXiv:2012.07805, 2020. find our card
  74. Sahaj Garg, Vincent Perot, Nicole Limtiaco, Ankur Taly, Ed H. Chi, and Alex Beutel. Counterfactual fairness in text classification through robustness. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 2019. ISBN 9781450363242. find
  75. Po-Sen Huang, Huan Zhang, Ray Jiang, Robert Stanforth, Johannes Welbl, Jack Rae, Vishal Maini, Dani Yogatama, and Pushmeet Kohli. Reducing sentiment bias in language models via counterfactual evaluation. In EMNLP (Findings), 2020. find
  76. Melvin Johnson. A scalable approach to reducing gender bias in google translate. https://ai.googleblog. com/2020/04/a-scalable-approach-to-reducing-gender.html, 2020. ↗
  77. Yusu Qian, Urwa Muaz, Ben Zhang, and Jae Won Hyun. Reducing gender bias in word-level language models with a gender-equalizing loss function. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, July 2019. find
  78. Paul Pu Liang, Irene Mengze Li, Emily Zheng, Yao Chong Lim, Ruslan Salakhutdinov, and Louis-Philippe Morency. Towards debiasing sentence representations. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, July 2020. find our card
  79. Margaret Li Y-Lan Boureau Jason Weston Emily Dinan Jing Xu, Da Ju. Recipes for safety in open-domain chatbots. arXiv preprint arXiv:2010.07079, 2020. find
  80. Alisa Liu, Maarten Sap, Ximing Lu, Swabha Swayamdipta, Chandra Bhagavatula, Noah A. Smith, and Yejin Choi. On-the-fly controlled text generation with experts and anti-experts. arXiv preprint arXiv:2105.03023, 2021. — [arXiv:2105.03023](https://arxiv.org/abs/2105.03023) find
  81. Jing Xu, Da Ju, Margaret Li, Y-Lan Boureau, Jason Weston, and Emily Dinan. Bot-adversarial dialogue for safe conversational agents. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2021. find
  82. Paul Pu Liang, Chiyu Wu, Louis-Philippe Morency, and Ruslan Salakhutdinov. Towards understanding and mitigating social biases in language models. In ICML, 2021. find our card
  83. Peter Henderson, Koustuv Sinha, Nicolas Angelard-Gontier, Nan Rosemary Ke, Genevieve Fried, Ryan Lowe, and Joelle Pineau. Ethical challenges in data-driven dialogue systems. In Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society, pages 123–129, 2018. find
  84. Emily Dinan, Angela Fan, Adina Williams, Jack Urbanek, Douwe Kiela, and Jason Weston. Queens are powerful too: Mitigating gender bias in dialogue generation. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2020. find our card
  85. Samuel Gehman, Suchin Gururangan, Maarten Sap, Yejin Choi, and Noah A. Smith. RealToxicityPrompts: Evaluating neural toxic degeneration in language models. In EMNLP (Findings), 2020. find our card
  86. Haochen Liu, Jamell Dacon, Wenqi Fan, Hui Liu, Zitao Liu, and Jiliang Tang. Does gender matter? towards fairness in dialogue systems. COLING, 2019. find
  87. Irene Solaiman and Christy Dennison. Process for adapting language models to society (PALMS) with values-targeted datasets. https://cdn.openai.com/palms.pdf, 2021. ↗
  88. Hannah Rashkin, Vitaly Nikolaev, Matthew Lamm, Michael Collins, Dipanjan Das, Slav Petrov, Gaurav Singh Tomar, Iulia Turc, and David Reitter. Measuring attribution in natural language generation models. arXiv preprint arXiv:2112.12870, 2021. find our card
  89. Or Honovich, Leshem Choshen, Roee Aharoni, Ella Neeman, Idan Szpektor, and Omri Abend. q 2 : Evaluating factual consistency in knowledge-grounded dialogues via question generation and question answering. arXiv preprint arXiv:2104.08202, 2021. find
  90. Taku Kudo and John Richardson. SentencePiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, 2018. find our card
  91. Rico Sennrich, Barry Haddow, and Alexandra Birch. Neural machine translation of rare words with subword units. arXiv preprint arXiv:1508.07909, 2015. find
  92. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. In NeurIPS, 2017. find
  93. Noam Shazeer. Glu variants improve transformer. arXiv preprint arXiv:2002.05202, 2020. find
  94. Jonathan Shen, Patrick Nguyen, Yonghui Wu, Zhifeng Chen, Mia X Chen, Ye Jia, Anjuli Kannan, Tara Sainath, Yuan Cao, Chung-Cheng Chiu, et al. Lingvo: a modular and scalable framework for sequence-to-sequence modeling. arXiv preprint arXiv:1902.08295, 2019. — [arXiv:1902.08295](https://arxiv.org/abs/1902.08295) find
  95. Yuanzhong Xu, HyoukJoong Lee, Dehao Chen, Blake A. Hechtman, Yanping Huang, Rahul Joshi, M. Krikun, Dmitry Lepikhin, Andy Ly, Marcello Maggioni, Ruoming Pang, Noam M. Shazeer, Shibo Wang, Tao Wang, Yonghui Wu, and Zhifeng Chen. Gspmd: General and scalable parallelization for ml computation graphs. a… — [arXiv:2105.04663](https://arxiv.org/abs/2105.04663) find
  96. Emily Dinan, Stephen Roller, Kurt Shuster, Angela Fan, Michael Auli, and J. Weston. Wizard of wikipedia: Knowledge-powered conversational agents. arXiv preprint arXiv:1811.01241, 2019. find
  97. Angeliki Lazaridou, Adhiguna Kuncoro, Elena Gribovskaya, Devang Agrawal, Adam Liska, Tayfun Terzi, Mai Gimenez, Cyprien de Masson d’Autume, Sebastian Ruder, Dani Yogatama, Kris Cao, Tomas Kocisky, Susannah Young, and Phil Blunsom. Pitfalls of static language modelling. arXiv preprint arXiv:2102.0195… find
  98. Spurthi Amba Hombaiah, Tao Chen, Mingyang Zhang, Michael Bendersky, and Marc Najork. Dynamic language models for continuously evolving content. arXiv preprint arXiv:2106.06297, 2021. — [arXiv:2106.06297](https://arxiv.org/abs/2106.06297) find
  99. Lora Aroyo and Chris Welty. Truth is a lie: Crowd truth and the seven myths of human annotation. AI Magazine, 36(1):15–24, Mar. 2015. doi: 10.1609/aimag.v36i1.2564. URL https://ojs.aaai.org/index. php/aimagazine/article/view/2564. ↗ our card
  100. Yi Chern Tan and L. Elisa Celis. Assessing social and intersectional biases in contextualized word representations. arXiv preprint arXiv:1911.01485, 2019. — [arXiv:1911.01485](https://arxiv.org/abs/1911.01485) find
  101. Nithya Sambasivan, Erin Arnesen, Ben Hutchinson, Tulsee Doshi, and Vinodkumar Prabhakaran. Re-imagining algorithmic fairness in india and beyond. arXiv preprint arXiv:2101.09995, 2021. — [arXiv:2101.09995](https://arxiv.org/abs/2101.09995) find
  102. Xiaodong Liu, Hao Cheng, Pengcheng He, Weizhu Chen, Yu Wang, Hoifung Poon, and Jianfeng Gao. Adversarial training for large neural language models. arXiv preprint arXiv:2004.08994, 2020. — [arXiv:2004.08994](https://arxiv.org/abs/2004.08994) find
  103. Joseph Weizenbaum. Computer Power and Human Reason: From Judgment to Calculation. W. H. Freeman & Co., New York, 1976. ISBN 0-7167-0463-3. find
  104. Zachary Kenton, Tom Everitt, Laura Weidinger, Iason Gabriel, Vladimir Mikulik, and Geoffrey Irving. Alignment of language agents. arXiv preprint arXiv:2103.14659, 2021. find
  105. Clifford Nass and Youngme Moon. Machines and mindlessness: Social responses to computers. Journal of Social Issues, 56:81–103, 03 2000. doi: 10.1111/0022-4537.00153. — [doi:10.1111/0022-4537.00153](https://doi.org/10.1111/0022-4537.00153) find
  106. Clifford Nass, Youngme Moon, and Nancy Green. Are machines gender neutral? gender-stereotypic responses to computers with voices. Journal of Applied Social Psychology, 27(10):864–876, 1997. doi: https://doi.org/ 10.1111/j.1559-1816.1997.tb00275.x. URL https://onlinelibrary.wiley.com/doi/abs/10.1111/… — [doi:10.1111/j.1559-1816.1997.tb00275.x](https://doi.org/10.1111/j.1559-1816.1997.tb00275.x) find
  107. Philippe Martin, Lorraine Cousin, Serge Gottot, Aurélie Bourmaud, Elise de La Rochebrochard, and Corinne Alberti. Participatory interventions for sexual health promotion for adolescents and young adults on the internet: Systematic review. Journal of Medical Internet Research, 22:e15378, 07 2020. find
  108. David Patterson, Joseph Gonzalez, Quoc Le, Chen Liang, Lluis-Miquel Munguia, Daniel Rothchild, David So, Maud Texier, and Jeff Dean. Carbon emissions and large neural network training. arXiv preprint arXiv:2104.10350, 2021. find our card
  109. Hector J. Levesque. The winograd schema challenge. In AAAI Spring Symposium: Logical Formalizations of Commonsense Reasoning, 2011. find