mlsanityData to 14 Oct 2026

Gpt-4: A Review on Advancements and Opportunities in Natural Language Processing

arXiv 2305.03195 ↗First posted 4 May 2023cs.CL cs.AI cs.CYcited by 15

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Abstract (arXiv)

Generative Pre-trained Transformer 4 (GPT-4) is the fourth-generation language model in the GPT series, developed by OpenAI, which promises significant advancements in the field of natural language processing (NLP). In this research article, we have discussed the features of GPT-4, its potential applications, and the challenges that it might face. We have also compared GPT-4 with its predecessor, GPT-3. GPT-4 has a larger model size (more than one trillion), better multilingual capabilities, improved contextual understanding, and reasoning capabilities than GPT-3. Some of the potential applications of GPT-4 include chatbots, personal assistants, language translation, text summarization, and question-answering. However, GPT-4 poses several challenges and limitations such as computational requirements, data requirements, and ethical concerns.

Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). Builds on 2 of its 5 references that are in the map. First posted on arXiv 4 May 2023.

Builds on

2 of 5 references.

References · 2

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2018GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understandingcited by 4,899

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 6; 2024: 5; 2025: 1; 2026: 3.

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  1. 2023Large Language Models for Information Retrieval: A Surveycited by 331
  2. 2023Large Language Models are Effective Text Rankers with Pairwise Ranking Promptingcited by 307
  3. 2023MechAgents: Large language model multi-agent collaborations can solve mechanics problems, generate new data, and integrate knowledgecited by 63
  4. 2023Machine Learning Without a Processor: Emergent Learning in a Nonlinear Electronic Metamaterialcited by 20
  5. 2024Evaluating LLM Reasoning in the Operations Research Domain with ORQAcited by 12
  6. 2024RAM: Towards an Ever-Improving Memory System by Learning from Communicationscited by 6
  7. 2023Inspire creativity with ORIBA: Transform Artists' Original Characters into Chatbots through Large Language Modelcited by 5
  8. 2024A Survey on Large Language Models from Concept to Implementationcited by 4
  9. 2023DeepDecipher: Accessing and Investigating Neuron Activation in Large Language Modelscited by 2
  10. 2025Harnessing intuitive local evolution rules for physical learningcited by 1
  11. 2026Orthogonal Hierarchical Decomposition for Structure-Aware Table Understanding with Large Language Modelscited by 1
  12. 2024PRECISE Framework: GPT-based Text For Improved Readability, Reliability, and Understandability of Radiology Reports For Patient-Centered Carecited by 0
3 more
  1. 2024Multi-method Integration with Confidence-based Weighting for Zero-shot Image Classificationcited by 0
  2. 2026Sparsity Induction for Accurate Post-Training Pruning of Large Language Modelscited by 0
  3. 2026Efficient Adaptation of LLMs for Hate Speech Detection in Low-Resource Languages: A Comparative Study on Roman Urducited by 0

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  1. 2023Evaluating the Logical Reasoning Ability of ChatGPT and GPT-4
  2. 2023GPTEval: A Survey on Assessments of ChatGPT and GPT-4
  3. 2023A Comprehensive Capability Analysis of GPT-3 and GPT-3.5 Series Models
  4. 2023Can GPT-4 Support Analysis of Textual Data in Tasks Requiring Highly Specialized Domain Expertise?
  5. 2023Summary of ChatGPT-Related Research and Perspective Towards the Future of Large Language Models
  6. 2024Putting GPT-4o to the Sword: A Comprehensive Evaluation of Language, Vision, Speech, and Multimodal Proficiency
  7. 2023MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models
  8. 2023GPT-3.5, GPT-4, or BARD? Evaluating LLMs Reasoning Ability in Zero-Shot Setting and Performance Boosting Through Prompts