A Bibliometric Review of Large Language Models Research from 2017 to 2023

Large language models (LLMs) are a class of language models that have demonstrated outstanding performance across a range of natural language processing (NLP) tasks and have become a highly sought-after research area, because of their ability to generate human-like language and their potential to revolutionize science and technology. In this study, we conduct bibliometric and discourse analyses of scholarly literature on LLMs. Synthesizing over 5,000 publications, this paper serves as a roadmap for researchers, practitioners, and policymakers to navigate the current landscape of LLMs research. We present the research trends from 2017 to early 2023, identifying patterns in research paradigms and collaborations. We start with analyzing the core algorithm developments and NLP tasks that are fundamental in LLMs research. We then investigate the applications of LLMs in various fields and domains including medicine, engineering, social science, and humanities. Our review also reveals the dynamic, fast-paced evolution of LLMs research. Overall, this paper offers valuable insights into the current state, impact, and potential of LLMs research and its applications.

Summary Notes

The Rising Influence of Large Language Models in Enterprise AI

The Evolution of LLMs

• From Basics to Breakthroughs: LLMs have come a long way from simple Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs) to advanced transformer architectures like BERT and GPT. This shift has significantly improved their ability to process and understand large volumes of text, making them a key component in AI.

Expanding Applications of LLMs

• Beyond Text Processing: Initially designed for natural language tasks, LLMs now power chatbots, virtual assistants, and translation tools. Their influence extends to healthcare for patient support, environmental science for data analysis, and social studies for trend analysis, showcasing their versatility and potential to tackle complex problems.

Key Developments in LLMs

• Milestones: The introduction of the Transformer architecture in 2017, BERT in 2018, and the subsequent releases of GPT-3 and GPT-4 have marked significant advancements in text generation and processing capabilities.

LLMs in Enterprise AI Solutions

• Data Privacy and Security: Protecting data confidentiality is crucial, particularly in sensitive sectors.

• Bias and Fairness: It's important to ensure the training data for these models does not perpetuate biases.

• Resource Efficiency: Managing the computational demands of LLMs is essential for sustainability.

Future Directions and Challenges

Conclusion

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