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

arXiv: https://arxiv.org/abs/2305.03195 · HTML: https://mlsanity.com/paper/2305.03195/

## 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 (most cited first, 2 of 2)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018)

## Built on by (most cited first, 15 of 15)

- [Large Language Models for Information Retrieval: A Survey](https://mlsanity.com/paper/2308.07107/) (2023)
- [Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting](https://mlsanity.com/paper/2306.17563/) (2023)
- [MechAgents: Large language model multi-agent collaborations can solve mechanics problems, generate new data, and integrate knowledge](https://mlsanity.com/paper/2311.08166/) (2023)
- [Machine Learning Without a Processor: Emergent Learning in a Nonlinear Electronic Metamaterial](https://mlsanity.com/paper/2311.00537/) (2023)
- [Evaluating LLM Reasoning in the Operations Research Domain with ORQA](https://mlsanity.com/paper/2412.17874/) (2024)
- [RAM: Towards an Ever-Improving Memory System by Learning from Communications](https://mlsanity.com/paper/2404.12045/) (2024)
- [Inspire creativity with ORIBA: Transform Artists' Original Characters into Chatbots through Large Language Model](https://mlsanity.com/paper/2306.09776/) (2023)
- [A Survey on Large Language Models from Concept to Implementation](https://mlsanity.com/paper/2403.18969/) (2024)
- [DeepDecipher: Accessing and Investigating Neuron Activation in Large Language Models](https://mlsanity.com/paper/2310.01870/) (2023)
- [Harnessing intuitive local evolution rules for physical learning](https://mlsanity.com/paper/2507.19561/) (2025)
- [Orthogonal Hierarchical Decomposition for Structure-Aware Table Understanding with Large Language Models](https://mlsanity.com/paper/2602.01969/) (2026)
- [PRECISE Framework: GPT-based Text For Improved Readability, Reliability, and Understandability of Radiology Reports For Patient-Centered Care](https://mlsanity.com/paper/2403.00788/) (2024)
- [Multi-method Integration with Confidence-based Weighting for Zero-shot Image Classification](https://mlsanity.com/paper/2405.02155/) (2024)
- [Sparsity Induction for Accurate Post-Training Pruning of Large Language Models](https://mlsanity.com/paper/2602.21652/) (2026)
- [Efficient Adaptation of LLMs for Hate Speech Detection in Low-Resource Languages: A Comparative Study on Roman Urdu](https://mlsanity.com/paper/2608.18142/) (2026)

## 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.