PaperlayerView exact revision on arXiv

Source-linked AI summary

Generative Pre-trained Transformer: A Comprehensive Review on Enabling Technologies, Potential Applications, Emerging Challenges, and Future Directions

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

arXiv:2305.10435v2cs.CLcs.AI

TL;DR

GPT models use transformer-based language modeling and staged training to generate natural-language responses. This review surveys their operation and applications while also identifying ethical, privacy, security, bias, and transparency challenges.

  • Problem

    Understanding GPT operation and applications matters because these models increasingly support interactions and services across domains while raising ethical and societal concerns.

  • Method

    The review explains GPT’s transformer architecture, language-model pre-training, response generation, fine-tuning, and reinforcement-learning stages, then surveys applications.

  • Results

    The review reports GPT applications in healthcare diagnosis, video-game dialogue generation, marketing communication, and personalized services.

  • Takeaways & Limitations

    GPT can support professional and consumer services, but responsible use requires attention to bias, transparency, privacy, security, and societal impact.

  • Takeaways & Limitations

    GPT outputs can reproduce training-data bias and remain difficult to interpret, which can undermine trust in their predictions.

Abstract

from arXiv · show

The Generative Pre-trained Transformer (GPT) represents a notable breakthrough in the domain of natural language processing, which is propelling us toward the development of machines that can understand and communicate using language in a manner that closely resembles that of humans. GPT is based on the transformer architecture, a deep neural network designed for natural language processing tasks. Due to their impressive performance on natural language processing tasks and ability to effectively converse, GPT have gained significant popularity among researchers and industrial communities, making them one of the most widely used and effective models in natural language processing and related fields, which motivated to conduct this review. This review provides a detailed overview of the GPT, including its architecture, working process, training procedures, enabling technologies, and its impact on various applications. In this review, we also explored the potential challenges and limitations of a GPT. Furthermore, we discuss potential solutions and future directions. Overall, this paper aims to provide a comprehensive understanding of GPT, enabling technologies, their impact on various applications, emerging challenges, and potential solutions.

I. INTRODUCTION

The introduction presents GPT as a transformer-based NLP breakthrough and motivates a comprehensive review because existing surveys address narrower topics. The review covers GPT technologies, applications, challenges, and future directions.

  • GPT uses deep learning and transformer self-attention to process context and support language generation, understanding, and task-specific applications.The review describes uses including sentiment analysis, language modeling, machine translation, text classification, code generation, and question answering.
  • Motivation and related surveys: Existing literature surveys GPT-related issues in academia, education, corporate communication, generative AI, and conversational AI, but not GPT comprehensively.
  • Review scope and organization: The review compares GPT architectures, enabling technologies, applications, projects, challenges, and future research directions.It uses literature from peer-reviewed journals, conferences, books, and major scholarly archives and databases.
  • Enabling progress: GPT advances are associated with architectural improvements, greater computing power, and fine-tuning techniques that broaden NLP capabilities.The paper connects these developments with applications in healthcare, customer service, and finance.
  • Contribution: The survey positions its comparison of existing surveys as a first-of-its-kind contribution providing extensive information on GPT models and related fields.

A. Generative Pre-trained Transformer

This section introduces GPT as an autoregressive deep-learning language model and traces its development from task-specific systems toward pretrained, broadly adaptable models. It also frames the survey’s comparison of versions and related limitations.

  • Definition: GPT generates human-like text from text input by modeling each present value from preceding values in an autoregressive process.
  • Limitations: The section identifies limitations including restricted output control, insufficiently diverse data, model size, limited understanding, robustness, explainability, and ethical, security, and privacy concerns.
  • Evolution of GPT: GPT-1 used unlabeled data for pretraining and could be fine-tuned, addressing the limited labeled-data and task-specificity constraints of earlier NLP models.
  • Evolution of GPT: GPT-3 and GPT-3.5 learned relationships among words, sentences, and components from large web-derived corpora including Wikipedia, social media, and news.

C. GPT model’s architecture

GPT’s architecture adapts the transformer into decoder blocks that use self-attention, embeddings, positional information, masking, and output probabilities. Pretraining predicts subsequent words, after which fine-tuning adapts parameters to downstream tasks.

  • Transformer architecture: GPT replaces transformer encoder-decoder blocks with decoder blocks and processes variable-length sequences using self-attention.
  • Pretraining: During pretraining, GPT uses unsupervised or self-supervised learning to predict the next word from preceding words and learn contextual relationships.
  • Input representation: Input embeddings map tokens to continuous vectors, while positional encoding supplies token-order information and masking restricts accessible context when required.
  • Fine-tuning: Fine-tuning changes pretrained parameters on a task-specific dataset to optimize performance for tasks such as classification or text generation.
  • Operational workflow: GPT operation is described as supervised fine-tuning, response generation, and proximal policy optimization with reinforcement learning.
  • Comparisons and limitations: The survey compares GPT versions and notes restrictions including limited output control, data diversity, robustness, explainability, and multimodal support.

III. ENABLING TECHNOLOGIES

GPT is enabled by converging data, AI, cloud, edge, networking, and human–computer interaction technologies. These technologies support training, optimization, deployment, accessibility, and task adaptation while introducing data, security, connectivity, and usability challenges.

  • Enabling technologies: GPT relies on big data, AI, cloud computing, edge computing, 5G and beyond networks, and HCI as enabling technologies.The review identifies these technologies as the major constituents of GPT models.
  • A. Big Data: Large and diverse datasets support GPT training, but data accuracy, privacy, and ethical use remain challenges.Training data may range from millions to trillions of items collected from books, articles, websites, and social media.
  • B. Artificial Intelligence: AI techniques support domain-specific fine-tuning, dialogue generation, and natural language understanding.Examples include training on legal or medical corpora, reinforcement learning for dialogue, and semantic parsing or named entity recognition.
  • C. Cloud Computing: Cloud computing provides scalable computational resources for training and operating GPT models across demand levels.Cloud platforms offer distributed computing, scalable storage, and security and compliance services.
  • C. Cloud Computing: GPT training and response optimization can use supervised fine-tuning, reward modeling, and PPO reinforcement learning.Labelers generate or rank responses, reward models evaluate them, and PPO updates the model policy.
  • C. Cloud Computing: Cloud dependence creates practical boundaries including connectivity failures, sensitive-data security risks, long-term costs, and variable availability.The review also notes that GPT operation requires substantial computing power and storage.

D. Edge Computing

Edge computing brings computation and storage closer to GPT users and data sources. The review links this arrangement to faster processing, lower latency, reduced bandwidth and transfer costs, and potentially stronger privacy protections.

  • D. Edge Computing: Edge computing decentralizes computational power and places storage and processing closer to consumers.This reduces the need for long-distance communication between clients and servers.
  • D. Edge Computing: For real-time GPT analysis, edge deployment can reduce response latency by avoiding repeated data movement between devices and the cloud.Edge accelerators such as GPUs and FPGAs can further speed GPT processing.
  • D. Edge Computing: Edge computing can lower bandwidth use and data-transfer expenses through local preprocessing, while keeping data nearer the periphery.The review presents this proximity as offering improved security and privacy protections for user requests.

E. 5G and beyond networks

The review presents 5G and beyond networks as infrastructure that can support faster GPT training, deployment, connectivity, and real-time responses. It also surveys GPT’s broad application impact, especially in education, while noting implementation and privacy concerns.

  • E. 5G and beyond networks: 5G and beyond networks offer faster transmission, lower latency, greater capacity, and more reliable connectivity for GPT systems.These capabilities can support larger models, faster training, and real-time communication with other devices or servers.
  • E. 5G and beyond networks: The effects of 5G depend on implementation and use, with cybersecurity, privacy, and infrastructure requirements remaining concerns.The review specifically notes risks from Internet access and GPT’s large-scale data analysis.
  • Impact of GPT: GPT is being applied across education, healthcare, agriculture, travel, e-commerce, entertainment, gaming, marketing, and finance.Listed uses include content creation, customer service, forecasting, diagnosis, crop analysis, logistics, and trading strategies.
  • 2) Impact of GPT in Education:: In education, GPT can generate educational content, summarize complex text, support writing, and enable intelligent tutoring.The review describes applications including textbooks, study guides, course materials, personalized feedback, and adaptive learning paths.
  • 2) Impact of GPT in Education:: Classroom studies reported promising results for using ChatGPT to evaluate assignments and generate practice problems, with potential instructor workload reduction.The authors state that these uses could improve content retention and understanding without compromising learning outcomes.

3) Challenges:

GPT applications offer educational benefits but also face limitations involving learning, human interaction, reliability, privacy, and integration. The review calls for further research on human–computer interaction and interfaces that support accurate and reliable educational use.

  • 3) Challenges:: GPT may impede students’ critical thinking and problem-solving skills and cannot provide a comprehensive understanding because it relies on statistical patterns.Learners who benefit from personal instructor interaction may also find reduced human involvement disadvantageous.
  • 3) Challenges:: Educational GPT systems raise concerns about data security, privacy, unreliable information, lack of citations, maintenance cost, and limited comprehension.The review also notes potential bias and the need to preserve human involvement in learning.
  • 3) Challenges:: Further research is needed on human–computer interaction and user-interface design for educational GPT workflows.The review emphasizes ensuring that generated information is accurate and reliable.

B. Healthcare

GPT is presented as a healthcare technology with applications spanning drug discovery, diagnosis, disease prediction, and personalized medicine. The section also emphasizes that healthcare deployment requires attention to bias, transparency, privacy, and clinical validation.

  • Drug Discovery: GPT can analyze chemical databases, reactions, and outcomes to suggest drug combinations and assess efficacy and toxicity.Its pattern-learning capability is applied to datasets of known compounds.
  • Diagnosis: GPT can support diagnosis by extracting patient information, analyzing multimodal inputs, and assisting clinicians rather than replacing medical decision-making.GPT-4 can analyze images and produce text, while GPT-based diagnosis and triage results varied across tasks and time.
  • Disease prediction: GPT can predict disease risk by learning patterns from patient records, medical images, and clinical-trial data.Examples include predicting diabetes, heart disease, and cancer.
  • Personalized medicine: GPT may support personalized medicine by using clinical, genomic, and nutritional data to tailor treatment, nutrition, exercise, and psychological recommendations.The passage describes personalized recommendations in obesity care.
  • Challenges: Healthcare GPT deployment faces risks from biased data, limited transparency, sensitive patient information, and limited clinical validation.These issues can contribute to incorrect predictions, reduced trust, privacy concerns, and uncertain adoption.
  • Summary: The healthcare discussion concludes that GPT could contribute to drug discovery, personalized medicine, decision support, diagnosis support, and disease prediction, while requiring continued evaluation of benefits and risks.Technology adoption, regulatory challenges, data bias, and security and privacy issues remain to be addressed.

3) Challenges:

The section describes GPT applications in industry and agriculture while emphasizing that practical benefits depend on responsible adaptation, adequate data, interpretability, infrastructure, and ethical safeguards. It identifies deployment cost and domain-specific constraints as important boundaries.

  • Challenges: Industrial GPT deployment requires benefit-cost assessment because implementation efforts raise deployment costs and create interpretability, data-reliance, and ethical concerns.Long-term policies are also described as necessary for sustainable production practices.
  • Summary: The broader conclusion is that GPT may benefit business operations and agriculture, but responsible use requires addressing ethical, interpretability, data, and implementation concerns.The stated potential includes higher productivity, improved yields, and more sustainable practices.
  • Agriculture: GPT can assist agriculture through crop cultivation knowledge, real-time decisions, disease and pest identification, precision farming, and resource allocation.Applications use historical, weather, soil, sensor, satellite, and IoT data to guide crop management.
  • Challenges: Agricultural GPT effectiveness depends on data quality and availability, while black-box decisions, infrastructure demands, language nuances, and privacy concerns constrain implementation.The section presents these constraints as requiring attention for practical and ethical deployment.

1) Introduction:

GPTs are presented as tools for personalizing and automating logistics, transportation, tourism, and e-commerce operations. The reviewed applications emphasize real-time information, route and inventory management, customer interaction, and content generation.

  • Travel and Transport: GPTs can personalize logistics recommendations by interpreting customer requirements and preferences in real time.They can recommend routes and transportation options using large amounts of data.
  • Travel and Transport: GPTs support shipping logistics through automated labels, real-time tracking, GPS and sensor integration, and supply-chain visibility.These capabilities are associated with greater automation, efficiency, and customer satisfaction.
  • Travel and Transport: GPTs can track fleets, identify possible maintenance needs, and support proactive fleet management.The passage describes analyzing data from multiple sources to detect problems before costly breakdowns or accidents.
  • Travel and Transport: GPTs provide real-time inventory access that supports stock decisions while reducing manual entry, carrying costs, and stockout-related lost sales.Cloud-based access enables inventory management from anywhere.
  • Travel and Transport: GPTs can optimize delivery routes using traffic, road conditions, delivery schedules, and other real-time data.The passage links route optimization with shorter travel times, improved delivery performance, and potentially lower idle time and trip distances.
  • Travel and Transport: In tourism, GPTs can provide natural-language travel assistance, personalized trip planning, destination information, safety guidance, and time-efficient routes.Users can request advice about destinations, transportation, customs, accommodations, restaurants, legislation, and emergency resources.
  • E-Commerce: In e-commerce, GPTs can answer questions about products, delivery, refunds, and other processes with rapid responses.The passage associates this with reduced customer waiting time, higher customer satisfaction, and lower support-worker workload.
  • E-Commerce: GPTs can proofread e-commerce text, including product descriptions, marketing materials, and customer reviews.Automated error detection and correction can save time and reduce misunderstandings or miscommunication.

3) Challenges:

The reviewed applications span e-commerce, entertainment, and content creation, while the section also identifies limitations involving context, real-time access, accuracy, latency, bias, plagiarism, and language barriers.

  • E-Commerce: GPTs may struggle to understand broader e-commerce context and cannot access real-time data or perform real-time calculations.Their responses rely mainly on input context, training data, and prior knowledge.
  • E-Commerce: Conversational GPT interfaces can personalize purchasing interactions and collect customer feedback about preferences, issues, and product opinions.The passage connects this feedback with identifying improvements, increasing customer happiness, and supporting data-driven decisions.
  • E-Commerce: GPTs may produce mistakes or poor answers for complicated or ambiguous e-commerce queries, requiring continual training, testing, monitoring, and feedback analysis.The passage calls for further research and testing before efficacy and dependability are confirmed.
  • Entertainment: In entertainment, GPTs provide immediate responses, poems, healing quotes, riddles, voice interaction, recommendations, personalized content, and gaming materials.The applications include solitude support, customer interaction, film and television assistance, influencer content, and realistic gaming interactions.
  • Entertainment: GPTs can generate game narratives, dialogues, characters, interfaces, recommendations, and code assistance for game developers.The passage describes these capabilities as supporting more realistic and user-specific gaming experiences.
  • Challenges: Voice-based GPT systems face latency, possible misinterpretations, difficult interruptions, bias, plagiarism, intellectual-property disputes, opaque content sources, and language barriers.The passage identifies edge computing, 5G, lifelong learning, improved multilingual capability, and clearer input requirements as areas for improvement.

4) Summary:

The summary presents GPTs as personalized assistants across entertainment, lifestyle, travel, fashion, cooking, hobbies, and career preparation. It also emphasizes unresolved concerns about trustworthiness, outdated information, nonsensical outputs, copyright, cost, and dependence.

  • Summary: GPTs are described as contributing to entertainment while raising concerns about job security, biased training data, source transparency, multilingual access, and plagiarism.The passage also identifies relating generated content to previous conversations and safer content generation as desired improvements.
  • Summary: GPTs provide human-like lifestyle assistance and personalized recommendations across diverse everyday activities.The review frames this assistance as relevant to lifestyle improvement and adaptation to cultural and technological shifts.
  • Summary: Diet-planning GPTs can generate meal plans, shopping lists, physical-activity plans, motivational messages, sleep patterns, and progress visualizations.Inputs may include fitness level, available time, medications, and exercise equipment.
  • Summary: Travel-planning GPTs can create itineraries from locations, budgets, and trip duration while recommending restaurants, hotels, and attractions.RoamAround, Roamr, and VacayChatbot are cited as examples.
  • Summary: GPTs can act as personalized stylists and cooking assistants by recommending clothing, wardrobes, recipes, ingredients, shopping lists, and nutritional information.Recommendations are described as adapting to occasions, seasons, dietary plans, available ingredients, time, and cooking skills.
  • Summary: GPTs can support hobbies and job seeking through activity selection, instructional resources, resumes, cover letters, interview training, and grooming sessions.The job-search passage describes multimodal assistance based on qualifications and experience.
  • Summary: Lifestyle GPTs may require specific input formats, use outdated databases, produce nonsensical information, lack access to specific job openings, and incur high development costs.The passage recommends further investigation before fully adopting their recommendations.
  • Summary: GPTs can enhance gaming through dialogue, storytelling, personalized worlds, character generation, chatbots, and content creation, although they are not specifically designed for games.The review also describes chatbot communication with players and GPT-2-generated quest-giver dialogue for role-playing games.

3) Challenges:

GPTs are discussed as marketing tools for content creation, customer service, advertising, and forecasting. The section also identifies control, bias, computational-resource, training-data, and game-environment challenges.

  • Challenges: Gaming deployments may require powerful hardware, large high-quality datasets, and greater control over content and interaction with the game environment.The review identifies computational expense, fragmented gaming data, limited content control, and restricted environmental interaction as challenges.
  • Marketing Applications: GPTs can generate marketing content, automate processes, provide insights, and support customer experiences.The review lists content creation, customer service, and personalized advertising as marketing applications.
  • Marketing Applications: GPTs can speed content creation, maintain consistency, personalize and translate material, and repurpose existing marketing content.They may be trained on company marketing materials and customer data to produce blogs, social posts, and product descriptions.
  • Marketing Applications: GPTs can generate natural customer-service responses continuously from customer-service conversations and chat logs.The passage presents this as a way to save personnel time and resources.
  • Marketing Applications: Applying GPT-3 to rationalize email communication was concluded to be technically and economically feasible.The described system interpreted email context before generating responses.
  • Marketing Applications: GPTs can tailor advertising content and segment customers using behavioral, interest, and preference data.Examples include product descriptions, blog posts, social-media captions, and targeted marketing campaigns.
  • Marketing Applications: GPTs can forecast customer behavior and buying patterns from past data to customize marketing campaigns.The cited application used ChatGPT for predictive modeling based on customer behavior and purchase patterns.
  • Challenges: Marketing GPTs may generate content inconsistent with brand image or message, and biased training data may produce biased content.The passage also characterizes GPT as complex and difficult to control.

4) Summary:

GPT applications span marketing, finance, and broader industry use, offering personalization, automation, analysis, and content generation. The review also emphasizes adoption challenges involving resources, interpretability, data, bias, security, and implementation.

  • GPTs support marketing through content creation, personalized messaging, increased efficiency, competitive advantage, and improved customer experience.
  • In finance, GPTs can automate customer support, assist fraud detection, provide investment insights, assess risk, analyze regulations, and support credit processes.
  • Finance applications: 98% accuracy was reported for aspect-based sentiment analysis using a lexicalized ontology to extract indirect relationships in user social data.
  • Finance challenges: Financial GPT adoption is constrained by computational expense, limited interpretability, training-data requirements, adversarial vulnerability, and bias risks.
  • Across applications, GPT changes content creation, personalized learning, market analysis, forecasting, and user interfaces while raising concerns about privacy, bias, security, and job loss.

A. SiriGPT

The reviewed projects demonstrate GPT applications in voice assistance, games, marketing, finance, business analytics, model deployment, meeting transcription, and dialogue systems. These projects combine GPT models with domain-specific data, interfaces, and operational pipelines.

  • A. SiriGPT: SiriGPT combines Siri voice commands with ChatGPT text generation through an OpenAI GPT-3 API and an Apple-optimized tokenizer.
  • B. AI Dungeon: AI Dungeon uses GPT-3 to generate user-directed characters and scenarios, but produced unsettling narratives including sexual content involving minors.
  • Copy.ai applies GPT-3 to business and marketing tasks including sales copy, long-form content, content reuse, product descriptions, and email writing.
  • Viable uses GPT-4 to extract sentiment and context from social posts, reviews, and surveys, providing interactive insights for business decisions.
  • AI Channels supports personalized model development from data preparation and training through deployment and monitoring, including APIs and Docker containers.
  • PLATO used language, dialogue, discrete-latent-variable, and response-ranking models, trained on over 40 GB of text and evaluated with BLEU, perplexity, and distinct n-gram metrics.

I. Jukebox

The reviewed projects illustrate GPT-based applications in music, recommendations, medical transcription, multilingual processing, and sustainable model development. The section also identifies open challenges involving domain adaptation, computation, interpretability, robustness, and modality support.

  • I. Jukebox: Jukebox extends GPT for music creation through Multi-Scale Transformers designed for the multi-scale structure of musical data.
  • Meena uses a seq2seq transformer architecture for open-domain conversation and personalized recommendations, pretrained on 341 GB of Reddit and social-platform text.
  • DeepScribe transcribes medical conversations so doctors can focus on patients rather than manually recording medical histories.
  • Polyglot AI combines multilingual pretraining with MUSE and techniques including masked language modeling and translation modeling for translation, sentiment analysis, and related tasks.
  • Facebook used Polyglot AI to translate between 100 languages, supporting communication across language barriers.
  • Open issues and future directions: Domain-specific GPT development remains challenging because suitable data are costly and heterogeneous, while adaptation can increase model size and cause forgetting.
  • GPT training and inference require increasingly high computational resources as model size and complexity grow, potentially taking days, weeks, or months.

C. Explainability and interpretability

The review identifies explainability, interpretability, bias, multimodal and multilingual support, robustness, context understanding, ethics, privacy, and security as major GPT challenges. It calls for models that are safer, more reliable, resource-efficient, and better adapted to domains and users.

  • C. Explainability and interpretability: GPTs are difficult to explain and interpret because their size and architecture obscure how outputs are produced and how internal processes operate.
  • Training-data bias can produce inaccurate representations and unfair outcomes, motivating data diversification, debiasing, architectural changes, and post-processing.
  • Multimodal GPT support remains unresolved because models designed primarily for text cannot fully process and generate audio, images, and video.
  • GPT robustness remains limited against unexpected or adversarial inputs, with proposed mitigations including adversarial training, defensive distillation, and regularization.
  • Multilingual support is difficult because languages differ substantially in syntax, grammar, and vocabulary.
  • GPT outputs may contain errors despite grammatical coherence because models have limited semantic and contextual understanding of nuance and figurative language.
  • Ethical concerns include bias, malicious use, employment effects, economic inequality, transparency, human involvement, and compliance with data-use regulations.
  • Security and privacy risks include convincing fake content and potential privacy violations, requiring safeguards before broad deployment.
Loading 2305.10435v2…