Computational Modeling of Language Learning in the Era of Generative Artificial Intelligence: A Response to Open Peer Commentaries

Language LearningEarly View IN PERSPECTIVE Computational Modeling of Language Learning in the Era of Generative Artificial Intelligence: A Response to Open Peer Commentaries This article relates to: Computational Modeling of Bilingual Language Learning: Current Models and Future Directions Ping Li, Qihui Xu, Language Learning First Published online: October 31, 2022 Qihui Xu, Qihui Xu orcid.org/0000-0002-5892-6442 Basque Centre on Cognition, Brain and LanguageSearch for more papers by this authorPing Li, Corresponding Author Ping Li [email protected] The Hong Kong Polytechnic University Correspondence concerning this article should be addressed to Ping Li, Department of Chinese and Bilingual Studies, Faculty of Humanities, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong. Email: [email protected]Search for more papers by this author Qihui Xu, Qihui Xu orcid.org/0000-0002-5892-6442 Basque Centre on Cognition, Brain and LanguageSearch for more papers by this authorPing Li, Corresponding Author Ping Li [email protected] The Hong Kong Polytechnic University Correspondence concerning this article should be addressed to Ping Li, Department of Chinese and Bilingual Studies, Faculty of Humanities, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong. Email: [email protected]Search for more papers by this author First published: 31 July 2023 https://doi.org/10.1111/lang.12605 Preparation of this article has been partially supported by the following grants: Hong Kong Research Grants Council (Project #PolyU15601520), Sin Wai Kin Foundation endowment grant, and the BERC 2022–2025 program Funded by the Spanish State Research Agency through BCBL Severo Ochoa excellence accreditation CEX2020-001010/AEI/10.13039/501100011033. We thank Jenny Geng for her editorial assistance with the manuscript. The handling editor for this article was Scott Crossley. Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat References Artetxe, M., Labaka, G., & Agirre, E. (2020). Translation artifacts in cross-lingual transfer learning. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (pp. 7674–7684). Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.emnlp-main.618 Binz, M., & Schulz, E. (2023). Using cognitive psychology to understand GPT-3. Proceedings of the National Academy of Sciences, 120(6), Article e2218523120. https://doi.org/10.1073/pnas.2218523120 Browning, J., Chalmers, D., Lake, B., LeCun, Y., Lupyan, G., & Pavlick, E. (2023, March 24). Do large language models require sensory grounding for meaning and understanding? [Pre-conference debate]. The Philosophy of Deep Learning. https://phildeeplearning.github.io Chomsky, N. (1981). Lectures on government and binding. Foris. Chomsky, N., Roberts, L., & Watumull, J. (2023, March 8). Noam Chomsky: The false promise of ChatGPT. The New York Times. https://www.nytimes.com/2023/03/08/opinion/noam-chomsky-chatgpt-ai.html Claussenius-Kalman, H., Hernandez, A. E., & Li, P. (2021). Expertise, ecosystem, and emergentism: Dynamic developmental bilingualism. Brain and Language, 222, Article 105013. https://doi.org/10.1016/j.bandl.2021.105013 Contreras Kallens, P., Kristensen-McLachlan, R. D., & Christiansen, M. H. (2023). Large language models demonstrate the potential of statistical learning in language. Cognitive Science, 47(3), Article e13256. https://doi.org/10.1111/cogs.13256 DeLuca, V., Rothman, J., Bialystok, E., & Pliatsikas, C. (2019). Redefining bilingualism as a spectrum of experiences that differentially affects brain structure and function. Proceedings of the National Academy of Sciences, 116(15), 7565–7574. https://doi.org/10.1073/pnas.1811513116 Dijkstra, T., & van Heuven, W. J. B. (2022). Inventing and reinventing the cog: A commentary on “Computational modeling of bilingual language learning: Current models and future directions.” Language Learning. Advance online publication. https://doi.org/10.1111/lang.12532 Elman, J. L. (1993). Learning and development in neural networks: The importance of starting small. Cognition, 48(1), 71–99. https://doi.org/10.1016/0010-0277(93)90058-4 Frank, M. C., Braginsky, M., Yurovsky, D., & Marchman, V. A. (2017). Wordbank: An open repository for developmental vocabulary data. Journal of Child Language, 44(3), 677–694. https://doi.org/10.1017/S0305000916000209 Goldstein, A., Zada, Z., Buchnik, E., Schain, M., Price, A., Aubrey, B., Nastase, S. A., Feder, A., Emanuel, D., Cohen, A., Jansen, A., Gazula, H., Choe, G., Rao, A., Kim, C., Casto, C., Fanda, L., Doyle, W., Friedman, D., … Hasson, U. (2022). Shared computational principles for language processing in humans and deep language models. Nature Neuroscience, 25(3), 369–380. https://doi.org/10.1038/s41593-022-01026-4 Grosjean, F. (2013). Bilingualism: A short introduction. In F. Grosjean & P. Li (Eds.), The psycholinguistics of bilingualism (pp. 5–21). Wiley-Blackwell. Jeong, Y., Cho, H.-Y., Kim, M., Oh, J.-P., Kang, M. S., Yoo, M., Lee, H.-S., & Han, J.-H. (2021). Synaptic plasticity-dependent competition rule influences memory formation. Nature Communications, 12(1), Article 3915. https://doi.org/10.1038/s41467-021-24269-4 Kachergis, G., Marchman, V. A., & Frank, M. C. (2022). Toward a “Standard Model” of early language learning. Current Directions in Psychological Science, 31(1), 20–27. https://doi.org/10.1177/09637214211057836 Kachergis, G., Tan, A. W. M., & Frank, M. C. (2022). Plurality is a good start, but it's time for unification: A commentary on “Computational modeling of bilingual language learning: Current models and future directions.” Language Learning. Advance online publication. https://doi.org/10.1111/lang.12531 Kriegeskorte, N., & Douglas, P. K. (2018). Cognitive computational neuroscience. Nature Neuroscience, 21(9), 1148–1160. https://doi.org/10.1038/s41593-018-0210-5 Kuhl, P. K., Tsao, F.-M., & Liu, H.-M. (2003). Foreign-language experience in infancy: Effects of short-term exposure and social interaction on phonetic learning. Proceedings of the National Academy of Sciences, 100(15), 9096–9101. https://doi.org/10.1073/pnas.1532872100 Legault, J., Zhao, J., Chi, Y.-A., Chen, W., Klippel, A., & Li, P. (2019). Immersive virtual reality as an effective tool for second language vocabulary learning. Languages, 4(1), Article 13. https://doi.org/10.3390/languages4010013 Lepori, M. A., Serre, T., & Pavlick, E. (2023). Break it down: Evidence for structural compositionality in neural networks. arXiv. http://arxiv.org/abs/2301.10884 Li, P., & Jeong, H. (2020). The social brain of language: Grounding second language learning in social interaction. Npj Science of Learning, 5(1), Article 8. https://doi.org/10.1038/s41539-020-0068-7 Li, P., & Lan, Y.-J. (2022). Digital language learning (DLL): Insights from behavior, cognition, and the brain. Bilingualism: Language and Cognition, 25(3), 361–378. https://doi.org/10.1017/S1366728921000353 Linck, J. A., Kroll, J. F., & Sunderman, G. (2009). Losing access to the native language while immersed in a second language: Evidence for the role of inhibition in second-language learning. Psychological Science, 20(12), 1507–1515. https://doi.org/10.1111/j.1467-9280.2009.02480.x MacWhinney, B. (2000). The CHILDES project: Tools for analyzing talk ( 3rd ed). Lawrence Erlbaum. Marian, V. (2022). To bilingualism and beyond! Modeling bilingualism requires looking beyond language: A commentary on “Computational modeling of bilingual language learning: Current models and future directions.” Language Learning. Advance online publication. https://doi.org/10.1111/lang.12530 Marjieh, R., Sucholutsky, I., van Rijn, P., Jacoby, N., & Griffiths, T. L. (2023). What language reveals about perception: Distilling psychophysical knowledge from large language models. arXiv. http://arxiv.org/abs/2302.01308 Mollo, D. C., & Millière, R. (2023). The vector grounding problem. arXiv. http://arxiv.org/abs/2304.01481 OpenAI. (2023). GPT-4 technical report. arXiv. http://arxiv.org/abs/2303.08774 Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C. L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., Schulman, J., Hilton, J., Kelton, F., Miller, L., Simens, M., Askell, A., Welinder, P., Christiano, P., Leike, J., & Lowe, R. (2022). Training language models to follow instructions with human feedback. arXiv. https://doi.org/10.48550/ARXIV.2203.02155 Peeters, D. (2019). Virtual reality: A game-changing method for the language sciences. Psychonomic Bulletin & Review, 26(3), 894–900. https://doi.org/10.3758/s13423-019-01571-3 Peñaloza, C., Grasemann, U., Miikkulainen, R., & Kiran, S. (2023). Modeling bilingualism as a dynamic phenomenon in healthy and neurologically affected speakers across the lifespan: A commentary on “Computational modeling of bilingual language learning: Current models and future directions.” Language Learning. Advance online publication. https://doi.org/10.1111/lang.12566 Peng, B., Alcaide, E., Anthony, Q., Albalak, A., Arcadinho, S., Cao, H., Cheng, X., Chung, M., Grella, M., GV, K. K., He, X., Hou, H., Kazienko, P., Kocon, J., Kong, J., Koptyra, B., Lau, H., Mantri, K. S. I., Mom, F., … Zhu, R.-J. (2023). RWKV: Reinventing RNNs for the transformer era. arXiv. http://arxiv.org/abs/2305.13048 Piantadosi, S. T. (2023). Modern language models refute Chomsky's approach to language. Lingbuzz Preprint, Article 7180. https://lingbuzz.net/lingbuzz/007180 Pinker, S. (1994). The language instinct. William Marrow. Plaut, D. C., McClelland, J. L., Seidenberg, M. S., & Patterson, K. (1996). Understanding normal and impaired word reading: Computational principles in quasi-regular domains. Psychological Review, 103(1), 56–115. https://doi.org/10.1037/0033-295X.103.1.56 Redcay, E., & Schilbach, L. (2019). Using second-person neuroscience to elucidate the mechanisms of social interaction. Nature Reviews Neuroscience, 20(8), 495–505. https://doi.org/10.1038/s41583-019-0179-4 Taori, R., Gulrajani, I., Zhang, T., Dubois, Y., Li, X., Guestrin, C., Liang, P., & Hashimoto, T. B. (n.d.). Alpaca: A strong, replicable instruction-following model. Stanford Center for Research on Foundation Models. https://crfm.stanford.edu/2023/03/13/alpaca.html Tomasello, M. (2004). Learning through others. Daedalus, 133(1), 51–58. https://www.jstor.org/stable/20027896 Tsoukala, C., Frank, S. L., & Broersma, M. (2017). “He's pregnant”: Simulating the confusing case of gender pronoun errors in L2 English. In G. Grunzelman & A. Howes (Eds.), Proceedings of the 39th Annual Conference of the Cognitive Science Society (pp. 3392–3397). https://www.mpi.nl/publications/item3009096/hes-pregnant-simulating-confusing-case-gender-pronoun-errors-l2-english Wu, S., & Dredze, M. (2019). Beto, Bentz, Becas: The surprising cross-lingual effectiveness of BERT. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) (pp. 833–844). Association for Computational Linguistics. https://doi.org/10.18653/v1/D19-1077 Xu, Q., Peng, Y., Wu, M., Xiao, F., Chodorow, M., & Li, P. (2023). Does conceptual representation require embodiment? Insights from large language models. arXiv. http://arxiv.org/abs/2305.19103 Yang, C. D. (2004). Universal grammar, statistics or both? Trends in Cognitive Sciences, 8(10), 451–456. https://doi.org/10.1016/j.tics.2004.08.006 Early ViewOnline Version of Record before inclusion in an issue ReferencesRelatedInformation