Natural Language Processing (NLP) has witnessed remarkable advancements in recent years, driven by the development of sophisticated algorithms and models such as GPT-4, T5, and BERT. These models have revolutionized the field by enabling machines to understand, generate, and interact with human language in ways that were previously unimaginable. This paper provides a comprehensive analysis of these state-of-the-art models, focusing on their application in automatic question generation for educational and assessment purposes. We explore the architectural innovations, training methodologies, and performance metrics that underpin the success of GPT-4, T5, and BERT in generating high-quality test questions. Additionally, we discuss the challenges associated with these models, including issues of bias, scalability, and interpretability. By examining the strengths and limitations of each model, this study aims to offer insights into the future directions of NLP and its potential to transform educational technologies. The findings highlight the importance of continuous innovation and rigorous evaluation in advancing the capabilities of NLP systems.
| Mualliflar | Салимов, Ж., Бойназаров, И.М. |
|---|---|
| Jurnal | Рақамли технологияларнинг назарий ва амалий масалалари |
| Nashr sanasi | 2025-04-06 |
| Jild | 8 |
| Son | 1 |
| Betlar | 138-143 |
| Til | Ingliz |
| DOI | 10.62132/ijdt.v8i1.242 |
DOI: 10.62132/ijdt.v8i1.242 · Maqolaning asl sahifasi
трансформаторная модель, глубокое обучение, масштабируемость, трансферное обучение, общий ползунок, GPT-4, transformer model, deep learning, scalability, transfer learning, common crawl, GPT-4
The article considers the problem of forecasting changes in groundwater levels using numerical methods based on a three-dimensional nonlinear mathematical model. The model takes into account factors affecting the…
In this article, a comparative analysis of various materials used in the manufacture of antenna devices is carried out. During the analysis, the properties of traditional, composite, and metamaterials were studied, and…
The article is devoted to the development of an information model of vocational guidance, focused on the analysis of competencies using the k-nearest neighbors (k-NN) method. In this work, the method is used to assess…
This article analyzes the vulnerabilities of the A5/1 stream cipher algorithm and proposes two new approaches to attack it. The first method is based on reconstructing the values of registers by analyzing the…
This study analyzes the efficiency of SHA (Secure Hash Algorithm) family algorithms depending on message length. The execution time of hashing processes for SHA-1 and SHA-2 (SHA-224, SHA-256, SHA-384, SHA-512)…
This article proposes a conceptual approach to optimizing decision-making processes in Business Intelligence systems based on the Pareto-optimal approach and the concept of self-adaptive agents. Business Intelligence…
This paper provides a comprehensive overview of optimal methods and processes for recording, processing, and classifying electromyography (EMG) signals in the context of human movement rehabilitation. It begins by…
A methodology has been developed for optimizing the identification, recognition, and classification of micro-objects based on dynamic models combined with neural networks with tools for filtering impulse interference…
As one of the important parts of face recognition, face image segmentation has become a major focus of human feature recognition. In this paper, the AdaBoost algorithm and Gabor texture analysis algorithm are used to…
This article focuses on developing a mathematical model intended for monitoring, forecasting, and guiding management decisions related to environmental protection in the surface layer of the atmosphere. The article…
Рақамли технологияларнинг назарий ва амалий масалалари — barcha maqolalar