Rationalism in the face of GPT hypes: Benchmarking the output of large language models against human expert-curated biomedical knowledge graphs

Author:

Babaiha Negin SadatORCID,Rao Sathvik Guru,Klein Jürgen,Schultz Bruce,Jacobs Marc,Hofmann-Apitius MartinORCID

Funder

Stiftung für das Bonn-Aachen International Center for Information Technology-B-IT

Publisher

Elsevier BV

Reference39 articles.

1. T. Kojima, S.S. Gu, M. Reid, Y. Matsuo, and Y. Iwasawa, “Large language models are zero-shot reasoners”.

2. ChatGPT: a comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope;Ray;Internet Things Cyber-Physical Syst,2023

3. “Recent advances in natural language processing via large pre-trained language models: a survey | ACM computing surveys.” Accessed: Nov. 22, 2023. Available: https://dl.acm.org/doi/full/10.1145/3605943?casa_token=ONsF3vqeb4cAAAAA%3AAyPn06seNfuPdWwhb8uUIzfo0Yt3zlqL0BZFvMgqzy97vm8BN2F5m-k-Z4_QdwiKkRB6OLICRd5sNg.

4. Q. Dong et al., “A survey on in-context learning.” arXiv, 2023. 10.48550/arXiv.2301.00234.

5. J. Wang et al., “Prompt engineering for healthcare: methodologies and applications.” arXiv, 2023. Accessed: Nov. 22, 2023. Available: http://arxiv.org/abs/2304.14670.

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