1. Van der Aa, H., Carmona Vargas, J., Leopold, H., Mendling, J., Padró, L.: Challenges and opportunities of applying natural language processing in business process management. In: COLING 2018: The 27th International Conference on Computational Linguistics: Proceedings of the Conference: August 20–26, 2018 Santa Fe, New Mexico, USA. pp. 2791–2801. Association for Computational Linguistics (2018)
2. Bender, E.M., Gebru, T., McMillan-Major, A., Shmitchell, S.: On the dangers of stochastic parrots: can language models be too big? In: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency. FAccT 2021, New York, NY, USA, pp. 610–623. Association for Computing Machinery (2021). https://doi.org/10.1145/3442188.3445922
3. Blagec, K., Kraiger, J., Frühwirt, W., Samwald, M.: Benchmark datasets driving artificial intelligence development fail to capture the needs of medical professionals. J. Biomed. Inform. 37, 104274 (2022)
4. Borji, A.: A categorical archive of ChatGPT failures (2023). https://doi.org/10.48550/ARXIV.2302.03494, https://arxiv.org/abs/2302.03494
5. Brown, T., et al.: Language models are few-shot learners. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M., Lin, H. (eds.) Advances in Neural Information Processing Systems, vol. 33, pp. 1877–1901. Curran Associates, Inc. (2020). https://proceedings.neurips.cc/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf