Keywords
Abstract
Ushbu maqolada katta til modellarida gallyutsinatsiya, ya’ni ishonarli ko‘rinadigan, lekin noto‘g‘ri yoki manbaga zid ma’lumot yaratish hodisasi tahlil qilinadi. Maqsad gallyutsinatsiyaning turlari va sabablarini aniqlash hamda uni kamaytirish yechimlarini tizimlashtirishdan iborat. Xalqaro va mahalliy ilmiy manbalar tahliliy va qiyosiy usulda o‘rganildi. Gallyutsinatsiya ma’lumotlar sifati, o‘qitish usuli, javob yaratish jarayoni va baholash mezonlari bilan bog‘liq omillar majmuasidan kelib chiqishi aniqlandi. Xulosa shuki, uni yagona usul bilan to‘liq bartaraf etib bo‘lmaydi, samarali yechim esa tashqi manbaga tayanish, javobni avtomatik tekshirish, baholashni takomillashtirish va inson nazoratini birlashtiruvchi ko‘p qatlamli yondashuvdir.
References
Bobojonova, L., Akhundjanova, A., Ostheimer, P. S., & Fellenz, S. (2025). BBPOS: BERT-based part-of-speech tagging for Uzbek. Proceedings of the First Workshop on Language Models for Low-Resource Languages, 287–293. Association for Computational Linguistics.
2. Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., & Liu, T. (2024). A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. ACM Transactions on Information Systems. https://doi.org/10.1145/3703155
3. Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., Ishii, E., Bang, Y. J., Madotto, A., & Fung, P. (2023). Survey of hallucination in natural language generation. ACM Computing Surveys, 55(12), Article 248.
4. Kalai, A. T., Nachum, O., Vempala, S. S., & Zhang, E. (2025). Why language models hallucinate. arXiv:2509.04664.
5. Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W., Rocktäschel, T., Riedel, S., & Kiela, D. (2020). Retrieval-augmented generation for knowledge-intensive NLP tasks. Advances in Neural Information Processing Systems, 33, 9459–9474.
6. Lin, S., Hilton, J., & Evans, O. (2022). TruthfulQA: Measuring how models mimic human falsehoods. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, 3214–3252.
7. Manakul, P., Liusie, A., & Gales, M. J. F. (2023). SelfCheckGPT: Zero-resource black-box hallucination detection for generative large language models. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 9004–9017.
8. Maynez, J., Narayan, S., Bohnet, B., & McDonald, R. (2020). On faithfulness and factuality in abstractive summarization. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 1906–1919.
9. Mukhamadiyev, A., Mukhiddinov, M., Khujayarov, I., Ochilov, M., & Cho, J. (2023). Development of language models for continuous Uzbek speech recognition system. Sensors, 23(3), 1145. https://doi.org/10.3390/s23031145
10. Xu, Z., Jain, S., & Kankanhalli, M. (2024). Hallucination is inevitable: An innate limitation of large language models. arXiv:2401.11817.
11. O‘zbekiston Respublikasi Prezidentining 2024-yil 14-oktabrdagi 358-son qarori «Sun’iy intellekt texnologiyalarini 2030-yilgacha rivojlantirish strategiyasini tasdiqlash to‘g‘risida». (2024).