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Large-language models (LLMs) such as GPT-4 caught the interest of many scientists.
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Kim, S.; Chen, J.; Cheng, T.; Gindulyte, A.; He, J.; He, S.; Li, Q.; Shoemaker, B. A.; Thiessen, P. A.; Yu, B.; Zaslavsky, L.; Zhang, J.; Bolton, E. E. PubChem 2023 update. Nucleic Acids Res. 2022
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Shi, J.; Albreiki, F.; Colón, Y. J.; Srivastava, S.; Whitmer, J. K. Transfer Learning Facilitates the Prediction of Polymer–Surface Adhesion Strength. J. Chem. Theory Comput. 2023
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Wolf, T. et al. Transformers: State-of-the-Art Natural Language Processing. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. 2020
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Bommasani, R. et al. On the Opportunities and Risks of Foundation Models. CoRR 2021
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Hu, E. J.; Shen, Y.; Wallis, P.; Allen-Zhu, Z.; Li, Y.; Wang, S.; Wang, L.; Chen, W. LoRA: Low-Rank Adaptation of Large Language Models. arXiv preprint: Arxiv-2106.09685 2021
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Selva Birunda, S.; Kanniga Devi, R. A review on word embedding techniques for text classification. Innovative Data Communication Technologies and Application: Proceedings of ICIDCA 2020 2021
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Suppiah, D. D.; Daud, W. M. A. W.; Johan, M. R. Supported Metal Oxide Catalysts for CO 2 \text{CO}{\vphantom{\text{X}}}_{\smash[t]{\text{2}}} Fischer–Tropsch Conversion to Liquid Fuels-A Review. Energy Fuels. 2021
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Eloundou, T.; Manning, S.; Mishkin, P.; Rock, D. GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models. arXiv preprint: Arxiv-2303.10130 2023
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Bubeck, S.; Chandrasekaran, V.; Eldan, R.; Gehrke, J.; Horvitz, E.; Kamar, E.; Lee, P.; Lee, Y. T.; Li, Y.; Lundberg, S.; Nori, H.; Palangi, H.; Ribeiro, M. T.; Zhang, Y. Sparks of Artificial General Intelligence: Early experiments with GPT-4. arXiv preprint: Arxiv-2303.12712 2023
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Shen, Y.; Song, K.; Tan, X.; Li, D.; Lu, W.; Zhuang, Y. HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in HuggingFace. arXiv preprint: Arxiv-2303.17580. 2023
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Liu, J. LlamaIndex. 2022; https://github.com/jerryjliu/llama_index , last accessed 2023-05-30
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Jablonka, K. M.; Schwaller, P.; Ortega-Guerrero, A.; Smit, B. Is GPT-3 all you need for low-data discovery in chemistry? ChemRxiv preprint 10.26434/chemrxiv-2023-fw8n4 2023
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White, A. D.; Hocky, G. M.; Gandhi, H. A.; Ansari, M.; Cox, S.; Wellawatte, G. P.; Sasmal, S.; Yang, Z.; Liu, K.; Singh, Y., et al. Assessment of chemistry knowledge in large language models that generate code. Digital Discovery 2023
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Ramos, M. C.; Michtavy, S. S.; Porosoff, M. D.; White, A. D. Bayesian Optimization of Catalysts With In-context Learning. arXiv preprint: Arxiv-2304.05341 2023
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Völker, C.; Benjami Moreno Torres,; Tehseen Rug,; Firdous, R.; Ghezal Ahmad,; Zia, J.; Lüders, S.; Scaffino, H. L.; Höpler, M.; Böhmer, F.; Pfaff, M.; Stephan, D.; Kruschwitz, S. Green building materials: a new frontier in data-driven sustainable concrete design. Preprint 10.13140/RG.2.2.29079.85925. 2023
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OpenAI, GPT-4 Technical Report. arXiv preprint: Arxiv-2303.08774v3. 2023
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Walker, N.; Dagdelen, J.; Cruse, K.; Lee, S.; Gleason, S.; Dunn, A.; Ceder, G.; Alivisatos, A. P.; Persson, K. A.; Jain, A. Extracting Structured Seed-Mediated Gold Nanorod Growth Procedures from Literature with GPT-3. arXiv preprint: Arxiv-2304.13846 2023
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Mamaghani, Z. G.; Hawboldt, K. A.; MacQuarrie, S. Adsorption of CO 2 \text{CO}{\vphantom{\text{X}}}_{\smash[t]{\text{2}}} using biochar - Review of the impact of gas mixtures and water on adsorption. J. Environ. Chem. Eng. 2023
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Mohsin, M.; Farhan, S.; Ahmad, N.; Raza, A. H.; Kayani, Z. N.; Jafri, S. H. M.; Raza, R. The electrochemical study of NixCe 1 – x O 2 – δ \text{NixCe}{\vphantom{\text{X}}}_{\smash[t]{\text{1\hskip 0.90417pt--\hskip 0.90417ptx\/}}}\text{O}{\vphantom{\text{X}}}_{\smash[t]{\text{2\hskip 0.90417pt--\hskip 0.90417pt$\delta$}}} electrodes using natural gas as a fuel. New J. Chem. 2023
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