KATTA TIL MODELLARI ASOSIDA TABIIY TILNI QAYTA ISHLASH TIZIMLARINING ZAMONAVIY MUAMMOLARI VA RIVOJLANISH ISTIQBOLLARI

Ushbu maqolada katta til modellari (Large Language Models – LLM) asosida tabiiy tilni qayta ishlash (Natural Language Processing – NLP) tizimlarining zamonaviy rivojlanish tendensiyalari, dolzarb muammolari va istiqbollari ilmiy jihatdan tahlil qilingan. So‘nggi yillarda Transformer arxitekturasi asosida yaratilgan ChatGPT, Gemini, LLaMA va boshqa generativ sun’iy intellekt tizimlari NLP sohasida tub burilish yasadi. Mazkur tizimlar matn yaratish, tarjima, savol-javob, semantik tahlil va dialog tizimlarini tashkil etishda yuqori natijalarni namoyish qilmoqda. Shu bilan birga, katta til modellari faoliyatida hallucination, ma’lumotlar xolisligi, etik muammolar, hisoblash resurslari sarfi hamda kam resursli tillarni qayta ishlash bilan bog‘liq qator cheklovlar mavjud. Maqolada LLM texnologiyalarining nazariy asoslari, ularning amaliy qo‘llanilishi va o‘zbek tili kabi kam resursli tillar uchun yuzaga kelayotgan muammolar tahlil qilingan. Tadqiqot natijalari NLP tizimlarini takomillashtirish, til modellarini lokal sharoitga moslashtirish va sun’iy intellektning xavfsiz hamda samarali qo‘llanilishiga doir ilmiy xulosalarni shakllantirishga xizmat qiladi.

This article scientifically analyzes current development trends, challenges, and future prospects of Natural Language Processing (NLP) systems based on Large Language Models (LLMs). In recent years, generative artificial intelligence systems built on Transformer architecture, such as ChatGPT, Gemini, and LLaMA, have significantly transformed the NLP field. These models demonstrate high performance in text generation, machine translation, question-answering systems, semantic analysis, and conversational agents. However, LLMs also face several challenges, including hallucination, data reliability, ethical concerns, high computational costs, and difficulties in processing low-resource languages. The article examines the theoretical foundations of LLMs, their practical applications, and challenges associated with processing low-resource languages such as Uzbek. The research findings contribute to improving NLP systems, adapting language models to local conditions, and ensuring the secure and effective implementation of artificial intelligence technologies.