Crosslingual conditional generation (e.g., machine translation) has long enjoyed the benefits of scaling.
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Miller, G. A · 1994
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Scaling laws for neural language models
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Interactive machine learning: letting users build classifiers
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Bleu: a method for automatic evaluation of machine translation
Papineni, K., Roukos, S., Ward, T., and Zhu, W.-J · 2002
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Interactive machine learning
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NLTK: The natural language toolkit
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Statistical significance tests for machine translation evaluation
Koehn, P · 2004
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MIMICS: A large-scale data collection for search clarification
Zamani, H., Lueck, G., Chen, E., Quispe, R., Luu, F., and Craswell, N · 2006
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Scaling laws for autoregressive generative modeling
Henighan, T., Kaplan, J., Katz, M., Chen, M., Hesse, C., Jackson, J., Jun, H., Brown, T. B., Dhariwal, P., Gray, S., Hallacy, C., Mann, B., Radford, A., Ramesh, A., Ryder, N., Ziegler, D. M., Schulman, J., Amodei, D., and McCandlish, S · 2010
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Interactive Question Answering , pp. 149 –
Konstantinova, N. and Orasan, C · 2013
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Power to the people: The role of humans in interactive machine learning
Amershi, S., Cakmak, M., Knox, W. B., and Kulesza, T · 2014
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Predictive translation memory: A mixed-initiative system for human language translation
Green, S., Chuang, J., Heer, J., and Manning, C. D · 2014
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Machine translation: Search queries at ebay, 2016
Badeka, T · 2016
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OpenSubtitles2016: Extracting large parallel corpora from movie and TV subtitles
Lison, P. and Tiedemann, J · 2016
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L. u., and Polosukhin, I · 2017
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Evaluating discourse phenomena in neural machine translation
Bawden, R., Sennrich, R., Birch, A., and Haddow, B · 2018
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Neural approaches to conversational AI
Gao, J., Galley, M., and Li, L · 2018
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A large-scale test set for the evaluation of context-aware pronoun translation in neural machine translation
Müller, M., Rios, A., Voita, E., and Sennrich, R · 2018
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Asking clarifying questions in open-domain information-seeking conversations
Aliannejadi, M., Zamani, H., Crestani, F., and Croft, W. B · 2019
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Nuanced metrics for measuring unintended bias with real data for text classification
Borkan, D., Dixon, L., Sorensen, J., Thain, N., and Vasserman, L · 2019
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BERT: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2019
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A survey on conversational agents/chatbots classification and design techniques
Hussain, S., Sianaki, O. A., and Ababneh, N · 2019
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Latent retrieval for weakly supervised open domain question answering
Lee, K., Chang, M.-W., and Toutanova, K · 2019
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INMT: Interactive neural machine translation prediction
Santy, S., Dandapat, S., Choudhury, M., and Bali, K · 2019
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Evaluating gender bias in machine translation
Stanovsky, G., Smith, N. A., and Zettlemoyer, L · 2019
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When a good translation is wrong in context: Context-aware machine translation improves on deixis, ellipsis, and lexical cohesion
Voita, E., Sennrich, R., and Titov, I · 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., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., and Amodei, D · 2020
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Resolving intent ambiguities by retrieving discriminative clarifying questions
Dhole, K. D · 2020
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AmbigQA: Answering ambiguous open-domain questions
Min, S., Michael, J., Hajishirzi, H., and Zettlemoyer, L · 2020
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Hernandez, D., Kaplan, J., Henighan, T., and McCandlish, S · 2021
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{GS}hard: Scaling giant models with conditional computation and automatic sharding
Lepikhin, D., Lee, H., Xu, Y., Chen, D., Firat, O., Huang, Y., Krikun, M., Shazeer, N., and Chen, Z · 2021
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Scaling language models: Methods, analysis & insights from training gopher
Rae, J. W., Borgeaud, S., Cai, T., Millican, K., Hoffmann, J., Song, H. F., Aslanides, J., Henderson, S., Ring, R., Young, S., Rutherford, E., Hennigan, T., Menick, J., Cassirer, A., Powell, R., van den Driessche, G., Hendricks, L. A., Rauh, M., Huang, P., Glaese, A., Welbl, J., Dathathri, S., Huang, S., Uesato, J., Mellor, J., Higgins, I., Creswell, A., McAleese, N., Wu, A., Elsen, E., Jayakumar, S. M., Buchatskaya, E., Budden, D., Sutherland, E., Simonyan, K., Paganini, M., Sifre, L., Martens, L., Li, X. L., Kuncoro, A., Nematzadeh, A., Gribovskaya, E., Donato, D., Lazaridou, A., Mensch, A., Lespiau, J., Tsimpoukelli, M., Grigorev, N., Fritz, D., Sottiaux, T., Pajarskas, M., Pohlen, T., Gong, Z., Toyama, D., de Masson d’Autume, C., Li, Y., Terzi, T., Mikulik, V., Babuschkin, I., Clark, A., de Las Casas, D., Guy, A., Jones, C., Bradbury, J., Johnson, M., Hechtman, B. A., Weidinger, L., Gabriel, I., Isaac, W. S., Lockhart, E., Osindero, S., Rimell, L., Dyer, C., Vinyals, O., Ayoub, K., Stanway, J., Bennett, L., Hassabis, D., Kavukcuoglu, K., and Irving, G · 2021
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Template-guided clarifying question generation for web search clarification
Wang, J. and Li, W · 2021
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mT5: A massively multilingual pre-trained text-to-text transformer
Xue, L., Constant, N., Roberts, A., Kale, M., Al-Rfou, R., Siddhant, A., Barua, A., and Raffel, C · 2021
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Palm: Scaling language modeling with pathways, 2022
Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H. W., Sutton, C., Gehrmann, S., Schuh, P., Shi, K., Tsvyashchenko, S., Maynez, J., Rao, A., Barnes, P., Tay, Y., Shazeer, N., Prabhakaran, V., Reif, E., Du, N., Hutchinson, B., Pope, R., Bradbury, J., Austin, J., Isard, M., Gur-Ari, G., Yin, P., Duke, T., Levskaya, A., Ghemawat, S., Dev, S., Michalewski, H., Garcia, X., Misra, V., Robinson, K., Fedus, L., Zhou, D., Ippolito, D., Luan, D., Lim, H., Zoph, B., Spiridonov, A., Sepassi, R., Dohan, D., Agrawal, S., Omernick, M., Dai, A. M., Pillai, T. S., Pellat, M., Lewkowycz, A., Moreira, E., Child, R., Polozov, O., Lee, K., Zhou, Z., Wang, X., Saeta, B., Diaz, M., Firat, O., Catasta, M., Wei, J., Meier-Hellstern, K., Eck, D., Dean, J., Petrov, S., and Fiedel, N · 2022
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Using natural language prompts for machine translation, 2022
Garcia, X. and Firat, O · 2022
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Scaling laws for neural machine translation
Ghorbani, B., Firat, O., Freitag, M., Bapna, A., Krikun, M., Garcia, X., Chelba, C., and Cherry, C · 2022
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Assistance with large language models
Krasheninnikov, D., Krasheninnikov, E., and Krueger, D · 2022
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Controlling translation formality using pre-trained multilingual language models
Rippeth, E., Agrawal, S., and Carpuat, M · 2022
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Language models are multilingual chain-of-thought reasoners, 2022
Shi, F., Suzgun, M., Freitag, M., Wang, X., Srivats, S., Vosoughi, S., Chung, H. W., Tay, Y., Ruder, S., Zhou, D., Das, D., and Wei, J · 2022
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Prompting palm for translation: Assessing strategies and performance, 2022
Vilar, D., Freitag, M., Cherry, C., Luo, J., Ratnakar, V., and Foster, G · 2022
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Measuring and mitigating name biases in neural machine translation
Wang, J., Rubinstein, B., and Cohn, T · 2022
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Ai chains: Transparent and controllable human-ai interaction by chaining large language model prompts
Wu, T., Terry, M., and Cai, C. J · 2022
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Least-to-most prompting enables complex reasoning in large language models, 2022
Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., and Chi, E · 2022
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Prompting large language model for machine translation: A case study, 2023
Zhang, B., Haddow, B., and Birch, A · 2023
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