Is ChatGPT a Biomedical Expert? -- Exploring the Zero-Shot Performance of Current GPT Models in Biomedical Tasks

SNOMED clinical terms: overview of the development process and project status.,

M. Q. Stearns, C. Price, K. A. Spackman, A. Y. Wang, · 2001

Earlier work this paper cites.

J. I. Tait, An Introduction to Professional Search, Springer International Publishing, Cham, 2014, pp. 1–5. URL: https://doi.org/10.1007/978-3-319-12511-4_1 . doi: 10.1007/978-3-319-12511-4_1

2014

Earlier work this paper cites.

Attention is All You Need,

A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, I. Polosukhin, · 2017

Earlier work this paper cites.

Searching the Enterprise,

U. Kruschwitz, C. Hull, · 2017

Earlier work this paper cites.

First international workshop on professional search,

S. Verberne, J. He, U. Kruschwitz, G. Wiggers, B. Larsen, T. Russell-Rose, A. P. de Vries, · 2018

Earlier work this paper cites.

SciBERT: A Pretrained Language Model for Scientific Text,

I. Beltagy, K. Lo, A. Cohan, · 2019

Earlier work this paper cites.

Language Models Are Few-Shot Learners,

B. et al., · 2020

Earlier work this paper cites.

BioBERT: a pre-trained biomedical language representation model for biomedical text mining,

J. Lee, W. Yoon, S. Kim, D. Kim, S. Kim, C. H. So, J. Kang, · 2020

Earlier work this paper cites.

Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing,

Y. Gu, R. Tinn, H. Cheng, M. R. Lucas, N. Usuyama, X. Liu, T. Naumann, J. Gao, H. Poon, · 2020

Earlier work this paper cites.

Interactive query expansion for professional search applications,

T. Russell-Rose, P. Gooch, U. Kruschwitz, · 2021

Earlier work this paper cites.

On the dangers of stochastic parrots: Can language models be too big?,

Original

E. M. Bender, T. Gebru, A. McMillan-Major, S. Shmitchell, · 2021

Cited alongside, same era.

Training language models to follow instructions with human feedback,

L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. Wainwright, P. Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Ray, et al., · 2022

Cited alongside, same era.

S. Zhang, S. Roller, N. Goyal, M. Artetxe, M. Chen, S. Chen, C. Dewan, M. Diab, X. Li, X. V. Lin, T. Mihaylov, M. Ott, S. Shleifer, K. Shuster, D. Simig, P. S. Koura, A. Sridhar, T. Wang, L. Zettlemoyer, OPT: Open Pre-trained Transformer Language Models, 2022. arXiv:2205.01068

Original

2022

Cited alongside, same era.

Search strategy formulation for systematic reviews: Issues, challenges and opportunities,

A. MacFarlane, T. Russell-Rose, F. Shokraneh, · 2022

Cited alongside, same era.

Mesh term suggestion for systematic review literature search,

S. Wang, H. Li, H. Scells, D. Locke, G. Zuccon, · 2022

S. Biderman, H. Schoelkopf, Q. Anthony, H. Bradley, K. O’Brien, E. Hallahan, M. A. Khan, S. Purohit, U. S. Prashanth, E. Raff, A. Skowron, L. Sutawika, O. van der Wal, Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling, 2023. arXiv:2304.01373

Original

2023

Closest in time.

T. Dettmers, A. Pagnoni, A. Holtzman, L. Zettlemoyer, QLoRA: Efficient Finetuning of Quantized LLMs, 2023. arXiv:2305.14314

Original

2023

Closest in time.

BioASQ-QA: A manually curated corpus for Biomedical Question Answering,

A. Krithara, A. Nentidis, K. Bougiatiotis, G. Paliouras, · 2023

Closest in time.

Overview of MedProcNER task on medical procedure detection and entity linking at BioASQ 2023,

S. Lima-López, E. Farré-Maduell, L. Gascó, A. Nentidis, A. Krithara, G. Katsimpras, G. Paliouras, M. Krallinger, · 2023

Closest in time.

Mesh suggester: A library and system for mesh term suggestion for systematic review boolean query construction,