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Summary of ChatGPT-Related Research and Perspective Towards the Future of Large Language Models

Summary

This survey maps the early wave of research on ChatGPT (GPT-3.5 and GPT-4) after its December 2022 release. The authors collected every arXiv paper that mentioned ChatGPT up to 1 April 2023, which came to 194 papers. They analysed this set in three ways: monthly submission trends, a word cloud of frequent terms, and a breakdown by application field.

The review then groups the reported uses into several areas:

  • question answering
  • text classification and generation
  • code generation
  • inference
  • data extraction and processing
  • human–ChatGPT collaboration
  • tool integration
  • medicine
  • AI ethics

It also covers studies that evaluate the model's capabilities and its limitations.

The analysis shows that work is concentrated on core NLP tasks. Education, history, mathematics, medicine and physics remain comparatively underexplored despite encouraging early findings. The authors attribute the model's versatility to web-scale pre-training, instruction fine-tuning and RLHF. They close with a research agenda covering data filtering, cheaper training, real-time data and local domain-specific deployment. For practitioners, the paper offers an early map of where LLMs help and where they fail.

Key points

  • The corpus is 194 arXiv papers mentioning ChatGPT, collected up to 1 April 2023. A live companion webpage tracks newer trends: snnubiai.github.io/chatgpt arxiv analysis
  • Mathematics: On the GHOSTS dataset of graduate-level problems, ChatGPT stayed below the level of typical math graduate students. It scored well on only 26% of the MATH subset and earned the lowest score on Holes-in-Proofs.
  • Physics: On the Force Concept Inventory, ChatGPT scored about 50–65%. A cohort of 415 students averaged 56%.
  • Math word problems and tutoring:
    • On DRAW-1K, introspection-based performance prediction raised accuracy from 34% to 69%.
    • In an OATutor study, 70% of ChatGPT-generated algebra hints passed manual quality checks. Human-written hints still scored higher.
  • Ethics and outlook: The listed risks include bias, misinformation, and privacy (ChatGPT was banned in Italy in April 2023). The proposed remedies include interdisciplinary ethical frameworks and localized LLM deployment in sensitive domains such as medicine.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Natural language processing · AI & machine learning

Cited by

89 papers of our library cite it (of the 140884 whose bibliographies we read); 7 of them have a page here, 82 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.1016/j.metrad.2023.100017 · arXiv 2304.01852

@misc{liu2023summary,
  title = {{Summary of ChatGPT-Related Research and Perspective Towards the Future of Large Language Models}},
  author = {Liu, Yiheng and Han, Tianle and Ma, Siyuan and Zhang, Jiayue and Yang, Yuanyuan and Tian, Jiaming and He, Hao and Li, Antong and He, Mengshen and Liu, Zhengliang and Wu, Zihao and Zhao, Lin and Zhu, Dajiang and Li, Xiang and Qiang, Ning and Shen, Dingang and Liu, Tianming and Ge, Bao},
  year = {2023},
  doi = {10.1016/j.metrad.2023.100017},
  eprint = {2304.01852},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.1016/j.metrad.2023.100017}
}

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  103. Yang, X., Li, Y., Zhang, X., Chen, H., Cheng, W.: Exploring the limits of chatgpt for query or aspect-based text summarization. arXiv preprint arXiv:2302.08081 (2023) find
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