Zobrazeno 1 - 10
of 46 211
pro vyhledávání: '"scientific language"'
Autor:
سید مهدی سمائی1 samai@irandoc.ac.ir, بهروز رسولی2 rasuli@irandoc.ac.ir
Publikováno v:
Academic Librarianship & Information Research. Jun2024, Vol. 58 Issue 2, p1-16. 16p.
Autor:
DJabri, Sara1 djabrisara88@gmail.com
Publikováno v:
Afak of Science Journal / Āfāq li-l-ՙulūm. 2024, Vol. 9 Issue 2, p28-37. 10p.
This Open Access book constitutes the refereed proceedings of the First International Workshop on Natural Scientific Language Processing and Research Knowledge Graphs, NSLP 2024, held in Hersonissos, Crete, Greece, on May 27, 2024. The 10 full papers
Efficient molecular modeling and design are crucial for the discovery and exploration of novel molecules, and the incorporation of deep learning methods has revolutionized this field. In particular, large language models (LLMs) offer a fresh approach
Externí odkaz:
http://arxiv.org/abs/2402.04119
SciInstruct: a Self-Reflective Instruction Annotated Dataset for Training Scientific Language Models
Autor:
Zhang, Dan, Hu, Ziniu, Zhoubian, Sining, Du, Zhengxiao, Yang, Kaiyu, Wang, Zihan, Yue, Yisong, Dong, Yuxiao, Tang, Jie
Large Language Models (LLMs) have shown promise in assisting scientific discovery. However, such applications are currently limited by LLMs' deficiencies in understanding intricate scientific concepts, deriving symbolic equations, and solving advance
Externí odkaz:
http://arxiv.org/abs/2401.07950
Akademický článek
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Akademický článek
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Autor:
Rauthmann, John F.1 (AUTHOR) jfrauthmann@gmail.com
Publikováno v:
European Journal of Personality. Nov2024, Vol. 38 Issue 6, p863-866. 4p.
Autor:
Golkar, Siavash, Pettee, Mariel, Eickenberg, Michael, Bietti, Alberto, Cranmer, Miles, Krawezik, Geraud, Lanusse, Francois, McCabe, Michael, Ohana, Ruben, Parker, Liam, Blancard, Bruno Régaldo-Saint, Tesileanu, Tiberiu, Cho, Kyunghyun, Ho, Shirley
Due in part to their discontinuous and discrete default encodings for numbers, Large Language Models (LLMs) have not yet been commonly used to process numerically-dense scientific datasets. Rendering datasets as text, however, could help aggregate di
Externí odkaz:
http://arxiv.org/abs/2310.02989
We present MatSci-NLP, a natural language benchmark for evaluating the performance of natural language processing (NLP) models on materials science text. We construct the benchmark from publicly available materials science text data to encompass seve
Externí odkaz:
http://arxiv.org/abs/2305.08264