ZAMONAVIY TILSHUNOSLIKDA SUN’IY INTELLEKT INTEGRATSIYASIDAGI MUAMMOLAR VA CHEKLOVLAR
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Mazkur maqolada zamonaviy tilshunoslikda sun’iy intellekt texnologiyalarining integratsiyasi jarayonida yuzaga kelayotgan muammolar
va cheklovlar tahlil qilinadi. Tadqiqotda tabiiy tilni qayta ishlash, katta til modellari, generativ sun’iy intellekt va mashinaviy tarjima
texnologiyalarining lingvistik imkoniyatlari hamda ularni qo‘llash jarayonida kuzatiladigan muammolar ilmiy manbalar asosida
o‘rganiladi. Xususan, lingvistik ma’lumotlarning yetarliligi va sifati, kam resursli tillarning raqamli muhitda yetarlicha ifodalanmasligi,
semantik va pragmatik ma’nolarni modellashtirish, ko‘p tillilik hamda lingvomadaniy xususiyatlarni hisobga olish masalalariga alohida
e’tibor qaratiladi.
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Linguistics, and Speech Recognition (3rd ed., draft). Pearson.
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58th Annual Meeting of the Association for Computational Linguistics, 5185–5198. https://doi.org/10.18653/v1/2020.acl-main.463
3. OpenAI, Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., et al. (2023). GPT-4 Technical Report. arXiv:2303.08774. (Texnik
hisobot; sahifa oralig‘i bibliografik nashr sahifalaridek ko‘rsatilmaydi.)
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big? Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 610–623.
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in Neural Information Processing Systems, 33, 1877–1901.
6. Manning, C. D. (2022). Human language understanding & reasoning. Daedalus, 151(2), 127–138. https://doi.org/10.1162/daed_a_01905
7. Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., et al. (2023). “So what if ChatGPT wrote it?” Multidisciplinary
perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International
Journal of Information Management, 71, Article 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642
8. Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., et al. (2023). ChatGPT for good? On opportunities and
challenges of large language models for education. Learning and Individual Differences, 103, Article 102274.
https://doi.org/10.1016/j.lindif.2023.102274
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10. Nigatu, H. H., Tonja, A. L., Rosman, B., Solorio, T., & Choudhury, M. (2024). The Zeno’s paradox of “low-resource” languages. Proceedings
of the 2024 Conference on Empirical Methods in Natural Language Processing, 17753–17774.
11. Cahyawijaya, S., Lovenia, H., & Fung, P. (2024). LLMs are few-shot in-context low-resource language learners. Proceedings of the 2024
Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 405–433.
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12. Chang, T. A., Arnett, C., Tu, Z., & Bergen, B. K. (2024). When is multilinguality a curse? Language modeling for 250 high- and low-
resource languages. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 4074–4096.
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of the 62nd Annual Meeting of the Association for Computational Linguistics, 16366–16393. https://doi.org/10.18653/v1/2024.acl-long.862
14. Benkirane, K., Gongas, L., Pelles, S., Fuchs, N., Darmon, J., Stenetorp, P., Adelani, D. I., & Sánchez, E. (2024). Machine translation
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