GPT (Generative Pre-Trained Transformer)— A Comprehensive Review on Enabling Technologies, Potential Applications, Emerging Challenges, and Future Directions

Yenduri, Gokul; Ramalingam, M.; Selvi, G. Chemmalar; Supriya, Y.; Srivastava, Gautam; Maddikunta, Praveen Kumar Reddy; Raj, G. Deepti; Jhaveri, Rutvij H.; Prabadevi, B.; Wang, Weizheng; Vasilakos, Athanasios V.; Gadekallu, Thippa Reddy

doi:10.1109/access.2024.3389497

Cited by 21 publications

(5 citation statements)

References 178 publications

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“…Students can engage in interactive learning, explore their curiosity and develop their language skills. Prompt engineering encourage discussions, provides problem solving scenarios, and enhance the development of metacognitive skills (Yenduri et al, 2023). This also provides personalized feedback and assessment and foster collaborative and interactive learning environments.…”

Section: Discussionmentioning

confidence: 99%

Promoting optimal learning with ChatGPT: A comprehensive exploration of prompt engineering in education

Leung

2024

5052

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The purpose of this paper is to study the topic of prompt engineering, which serves as a valuable tool for teachers in creating optimal prompts that effectively enhance students' learning experiences with ChatGPT. This paper explores a variety of strategies related to prompt engineering. These strategies include assigning specific roles to ChatGPT, clearly defining objectives, applying constraints, utilizing structural prompt formats, refining answers through dialogues, and integrating practice exercises. Moreover, this paper specifically delves into relevant approaches to prompt engineering in the field of education, such as close question prompts, open question prompts, role-playing prompts, and Socratic prompts. It also presents the outcomes derived from a comprehensive survey conducted to assess teachers' attitudes towards the implementation of prompt engineering with ChatGPT. The collected data indicates that prompt engineering significantly contributes to the enhancement of the learning experience. This is achieved by tailoring prompts to suit individual needs, fostering greater engagement, promoting critical thinking skills, and facilitating collaborative and interactive learning environments. The findings of this study hold significant practical implications for educators. By effectively implementing prompt engineering strategies, teachers can fully harness the potential of ChatGPT to enhance students' learning experiences. By customizing prompts to individual students, educators can foster engagement, stimulate reasoning, and facilitate collaboration among students.

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Section: Discussionmentioning

confidence: 99%

Promoting optimal learning with ChatGPT: A comprehensive exploration of prompt engineering in education

Leung

2024

5052

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“…predicting the next word based on previous words. Thus, the model can learn natural language representations that can be fine-tuned for specific subtasks [24]. Features of the GPT models' versions are given in Table 2.…”

Section: Text Generation Modelsmentioning

confidence: 99%

Performance Comparison of Large Language Models, GPT and Gemini on Turkish News Classification Task

Guven

2024

Preprint

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Recently, large language models-LLMs have become very popular in many tasks of natural language processing. Examples of these tasks include text classification, question answering, text summarization, and text generation for natural language processing. Apart from LLMs, GPT and Gemini models are at the top of the list in terms of use for text generation tasks. This study aims to contribute to the literature on the use and comparison of LLMs and text generation models for the Turkish language. To achieve this purpose, the dataset consisting of Turkish news was classified by training BERT, ALBERT, DistilBERT, ELECTRA, XLM-RoBERTA LLMs with fine-tuning. Additionally, GPT-3.5 and Gemini text generation models were used by sending prompts for this classification task, and the success of the models was compared with LLMs. As a result of all analyses, the BERT model gave 97.619% accuracy among LLMs, while Gemini gave 99.167% accuracy among text generation models.

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“…BERT models, on the other hand, are designed to understand the deep contextual relationships between words in a sentence. This bidirectional training makes BERT models ideal for tasks like question answering, sentiment analysis, and natural language understanding [74,75].…”

Section: Foundations Of ML For Chatbotsmentioning

confidence: 99%

Error Correction and Adaptation in Conversational AI: A Review of Techniques and Applications in Chatbots

Izadi,

Forouzanfar

2024

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This study explores the progress of chatbot technology, focusing on the aspect of error correction to enhance these smart conversational tools. Chatbots, powered by artificial intelligence (AI), are increasingly prevalent across industries such as customer service, healthcare, e-commerce, and education. Despite their use and increasing complexity, chatbots are prone to errors like misunderstandings, inappropriate responses, and factual inaccuracies. These issues can have an impact on user satisfaction and trust. This research provides an overview of chatbots, conducts an analysis of errors they encounter, and examines different approaches to rectifying these errors. These approaches include using data-driven feedback loops, involving humans in the learning process, and adjusting through learning methods like reinforcement learning, supervised learning, unsupervised learning, semi-supervised learning, and meta-learning. Through real life examples and case studies in different fields, we explore how these strategies are implemented. Looking ahead, we explore the different challenges faced by AI-powered chatbots, including ethical considerations and biases during implementation. Furthermore, we explore the transformative potential of new technological advancements, such as explainable AI models, autonomous content generation algorithms (e.g., generative adversarial networks), and quantum computing to enhance chatbot training. Our research provides information for developers and researchers looking to improve chatbot capabilities, which can be applied in service and support industries to effectively address user requirements.

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GPT (Generative Pre-Trained Transformer)— A Comprehensive Review on Enabling Technologies, Potential Applications, Emerging Challenges, and Future Directions

Cited by 21 publications

References 178 publications

Promoting optimal learning with ChatGPT: A comprehensive exploration of prompt engineering in education

Promoting optimal learning with ChatGPT: A comprehensive exploration of prompt engineering in education

Performance Comparison of Large Language Models, GPT and Gemini on Turkish News Classification Task

Error Correction and Adaptation in Conversational AI: A Review of Techniques and Applications in Chatbots

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