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sides these challenges, domain-specific models require higher computation costs for the resources and time spent in pre- training and relearning in downstream tasks during fine-tuning of pre-trained domain-specific models. Therefore, domain- specific model development must focus on optimizing resource consumption and fine-tuning the pre-trained model to alleviate the forgetting problem involved in existing models. as they are customized to specific domain concerns and can provide more concise and informative solutions. TL can be used for developing domain-specific GPT models. Domain- specific GPT models were developed to summarize products based on customer reviews on an E-commerce site, where the language model is pre-trained on the Chinese-short summa- rization dataset and has obtained fine-tuned results [130]. Be-

Figure 9 sides these challenges, domain-specific models require higher computation costs for the resources and time spent in pre- training and relearning in downstream tasks during fine-tuning of pre-trained domain-specific models. Therefore, domain- specific model development must focus on optimizing resource consumption and fine-tuning the pre-trained model to alleviate the forgetting problem involved in existing models. as they are customized to specific domain concerns and can provide more concise and informative solutions. TL can be used for developing domain-specific GPT models. Domain- specific GPT models were developed to summarize products based on customer reviews on an E-commerce site, where the language model is pre-trained on the Chinese-short summa- rization dataset and has obtained fine-tuned results [130]. Be-

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LIST OF KEY ACRONYMS ONLY IF IT IS REPEATED
LIST OF KEY ACRONYMS ONLY IF IT IS REPEATED
turning points in the growth of the GPT model: Before GPT, NLP models have been trained on large amounts of annotated data that is related to a specific task. This had a significant drawback because it was difficult to access the quantity of labelled data required to train the model precisely. The NLP models were unable to complete tasks outside of their training set since they were restricted to a particluar set of data. To get around these restrictions, OpenAI offered a Generative Language Model called GPT-1 that was created using un- labeled data and then given to users to fine-tune to carry out subsequent tasks like sentiment analysis, categorization, and question-answering [18]. This indicates that the model attempts to produce an appropriate response based on input and that the data used to train the model is not labelled [19]. Fig. 2 shows the timeline of the evolution of several pre-trained models from Eliza, which was created in 1960, to the more current 2022-ChatGPT. lp 1 vce Few Greck ccs searcAaal #hak® well ew.n4 fiw 4aoh eee employs a 12-layer decoder architecture transformer with a self-attention system for training. GPT-1’s capacity to execute zero-shot performance on different tasks was one of its major success as a result of its pre-training. This ability demonstrated that generative language modelling can be used to generalize the model when combined with a successful pretraining idea. With TL as its foundation, GPT models evolved into a potent tool for performing NLP tasks with minimal fine-tuning [22]. It paved the way for other models to progress even more in generative pre-training using larger datasets and parameters. [18].
turning points in the growth of the GPT model: Before GPT, NLP models have been trained on large amounts of annotated data that is related to a specific task. This had a significant drawback because it was difficult to access the quantity of labelled data required to train the model precisely. The NLP models were unable to complete tasks outside of their training set since they were restricted to a particluar set of data. To get around these restrictions, OpenAI offered a Generative Language Model called GPT-1 that was created using un- labeled data and then given to users to fine-tune to carry out subsequent tasks like sentiment analysis, categorization, and question-answering [18]. This indicates that the model attempts to produce an appropriate response based on input and that the data used to train the model is not labelled [19]. Fig. 2 shows the timeline of the evolution of several pre-trained models from Eliza, which was created in 1960, to the more current 2022-ChatGPT. lp 1 vce Few Greck ccs searcAaal #hak® well ew.n4 fiw 4aoh eee employs a 12-layer decoder architecture transformer with a self-attention system for training. GPT-1’s capacity to execute zero-shot performance on different tasks was one of its major success as a result of its pre-training. This ability demonstrated that generative language modelling can be used to generalize the model when combined with a successful pretraining idea. With TL as its foundation, GPT models evolved into a potent tool for performing NLP tasks with minimal fine-tuning [22]. It paved the way for other models to progress even more in generative pre-training using larger datasets and parameters. [18].
and news items, GPT 3.5 learned the relations between words, sentences, and various components. It was utilized by OpenAI to develop several systems that have been tailored to complete particular jobs [26]. It collected vast amounts of data from the web, including tens of thousands of Wikipedia entries, posts on social media, and news items, and used that information to learn the relationships between sentences, words, and word components [29]. GPT-3 eventually emerged as OpenAI’s ground-breaking AI language software. Simply put, it is a piece of software that can create lines on its own that are so distinctive they almost sound like they were written by a human [25]. The GPT-3 program is presently accessible with limited access via a cloud-based API, and access is required to investigate the utility. Since its debut, it has produced several interesting apps. Its capacity, which is about 175 billion parameters big and 100 times larger than GPT-2, is a key advantage. It is taught using a corpus of 500 billion words called *Common Crawl” that was gathered from a sizable content archive and the internet [26]. Its other noteworthy and unexpected capability is its ability to carry out basic mathematical operations, write bits of code, and carry out clever tasks. As a result, NLP models can help businesses by responding more quickly to requests and accurately keeping best practices while minimizing human mistakes [27]. Due to its intricacy and size, many academics and writers have referred to it as the ultimate black-box AI method. Due to the high cost and inconvenience of performing inference, as well as the billion-parameter size that makes it resource-intensive, it is difficult to put into practice in jobs [24]. GPT_A wasc named ac the cucrececor nf GPT_2 Tn the meantime
and news items, GPT 3.5 learned the relations between words, sentences, and various components. It was utilized by OpenAI to develop several systems that have been tailored to complete particular jobs [26]. It collected vast amounts of data from the web, including tens of thousands of Wikipedia entries, posts on social media, and news items, and used that information to learn the relationships between sentences, words, and word components [29]. GPT-3 eventually emerged as OpenAI’s ground-breaking AI language software. Simply put, it is a piece of software that can create lines on its own that are so distinctive they almost sound like they were written by a human [25]. The GPT-3 program is presently accessible with limited access via a cloud-based API, and access is required to investigate the utility. Since its debut, it has produced several interesting apps. Its capacity, which is about 175 billion parameters big and 100 times larger than GPT-2, is a key advantage. It is taught using a corpus of 500 billion words called *Common Crawl” that was gathered from a sizable content archive and the internet [26]. Its other noteworthy and unexpected capability is its ability to carry out basic mathematical operations, write bits of code, and carry out clever tasks. As a result, NLP models can help businesses by responding more quickly to requests and accurately keeping best practices while minimizing human mistakes [27]. Due to its intricacy and size, many academics and writers have referred to it as the ultimate black-box AI method. Due to the high cost and inconvenience of performing inference, as well as the billion-parameter size that makes it resource-intensive, it is difficult to put into practice in jobs [24]. GPT_A wasc named ac the cucrececor nf GPT_2 Tn the meantime
COMPARSION OF DIFFERENT VERSIONS OF GPT MODEL TABLE III Generative AI (GAI) models are of different types like uni- modal, cross-modal, and multimodal. The first type is uni- modal which rely on a single type of input, such as text or images. The cross-modal, on the other hand, can process multiple types of inputs and relate them to each other. The Multimodal is the most complex type of AI as it can process and integrate information from multiple modalities, such as speech, text, images, and even physical interactions with the environment. GPT adopts only unimodal and multimodal types where ChatGPT is said to be unimodal, while GPT-4 is multimodal. Fig. 6 is an illustration that distinguishes between unimodal, cross-modal, and multimodal Generative AI models. yielded valuable insights for the use of advanced models like GPT in the field of NLP [42]. The GPT models utilize DL and big data for natural language generation, with GPT-4 being the most advanced model to date [43]. The training data for GPT models typically include millions or even trillions of data from a diverse range of sources, such as books, articles, websites, and social media platforms. This arge and diverse training data helps GPT models capture the variations in language usage, making them more accurate and effective at NLP tasks. As a result, GPT models may be used for a variety of tasks, including question-answering, text summarization, and language translation [44]. Moreover, since GPT models can learn from a variety of data sources, they can be tuned for certain tasks and domains, making them very adaptive and versatile. GPT model has the potential to be utilized for a variety of activities, including the creation of images and videos in addition to its excellent language processing capabilities [45].
COMPARSION OF DIFFERENT VERSIONS OF GPT MODEL TABLE III Generative AI (GAI) models are of different types like uni- modal, cross-modal, and multimodal. The first type is uni- modal which rely on a single type of input, such as text or images. The cross-modal, on the other hand, can process multiple types of inputs and relate them to each other. The Multimodal is the most complex type of AI as it can process and integrate information from multiple modalities, such as speech, text, images, and even physical interactions with the environment. GPT adopts only unimodal and multimodal types where ChatGPT is said to be unimodal, while GPT-4 is multimodal. Fig. 6 is an illustration that distinguishes between unimodal, cross-modal, and multimodal Generative AI models. yielded valuable insights for the use of advanced models like GPT in the field of NLP [42]. The GPT models utilize DL and big data for natural language generation, with GPT-4 being the most advanced model to date [43]. The training data for GPT models typically include millions or even trillions of data from a diverse range of sources, such as books, articles, websites, and social media platforms. This arge and diverse training data helps GPT models capture the variations in language usage, making them more accurate and effective at NLP tasks. As a result, GPT models may be used for a variety of tasks, including question-answering, text summarization, and language translation [44]. Moreover, since GPT models can learn from a variety of data sources, they can be tuned for certain tasks and domains, making them very adaptive and versatile. GPT model has the potential to be utilized for a variety of activities, including the creation of images and videos in addition to its excellent language processing capabilities [45].
Fig. 3. Transformer Architecture.
Fig. 3. Transformer Architecture.
Fig. 4. Transformer Architecture and Input Transformations for Fine-Tuning on Different Tasks.
Fig. 4. Transformer Architecture and Input Transformations for Fine-Tuning on Different Tasks.
In this step unseen input sequences are passed to the clone SFT model, pass the responses to the reward model for understanding, how high quality was this response for that input
In this step unseen input sequences are passed to the clone SFT model, pass the responses to the reward model for understanding, how high quality was this response for that input
Fig. 6. A comparison between unimodal, cross-modal, and multimodal modal GPTs.
Fig. 6. A comparison between unimodal, cross-modal, and multimodal modal GPTs.
Fig. 7. Enabling technologies of GPT models.
Fig. 7. Enabling technologies of GPT models.
Fig. 8. The impact of GPT models on various applications.
Fig. 8. The impact of GPT models on various applications.
PROJECT SUMMARY TABLE (CONTINUED). itself on providing AI technology centred around human needs. This innovative project offers a product named BondBot, which is powered by Empathy Engine 3.0 and ChatGPT, to enhance the financial health of clients. It assists financial insti- tutions and employers in promoting interconnected offering various tools to improve the institution’s and the financial health of its clients on a sing finance by profitability e network. It uses customer data to create individual personas for every bank customer or small business, considering their strengths, and potential needs. This approach enab form to develop multiple customized pathways to enhance clients’ financial well-being. behaviours, es the plat- holistically data sources, such as social media posts, customer reviews, and survey res comprehending resulting in va ponses, by employing Viable. GPT assists in the sentiment and context behind the data, uable insights that can enhance a company’s services, products, and customer experience. Viable’s ’Insight Explorer” is a distinctive feature that enables users to interact with and visua ize their data via a user-friendly interface. In addition, the p atform offers advanced analytics capabilities, including entity recognition, topic modelling, and sentiment analysis. The GPT-based technology of Viable is continually evolving and advancing, which allows the platform to deliver more precise and insightful data. Moreover, Viable provides customized integration and solutions to cater to the specific requirements of each business.
PROJECT SUMMARY TABLE (CONTINUED). itself on providing AI technology centred around human needs. This innovative project offers a product named BondBot, which is powered by Empathy Engine 3.0 and ChatGPT, to enhance the financial health of clients. It assists financial insti- tutions and employers in promoting interconnected offering various tools to improve the institution’s and the financial health of its clients on a sing finance by profitability e network. It uses customer data to create individual personas for every bank customer or small business, considering their strengths, and potential needs. This approach enab form to develop multiple customized pathways to enhance clients’ financial well-being. behaviours, es the plat- holistically data sources, such as social media posts, customer reviews, and survey res comprehending resulting in va ponses, by employing Viable. GPT assists in the sentiment and context behind the data, uable insights that can enhance a company’s services, products, and customer experience. Viable’s ’Insight Explorer” is a distinctive feature that enables users to interact with and visua ize their data via a user-friendly interface. In addition, the p atform offers advanced analytics capabilities, including entity recognition, topic modelling, and sentiment analysis. The GPT-based technology of Viable is continually evolving and advancing, which allows the platform to deliver more precise and insightful data. Moreover, Viable provides customized integration and solutions to cater to the specific requirements of each business.
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