Mining experimental data from materials science literature with large language models: an evaluation study
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| Vydané v: | Science and Technology of Advanced Materials: Methods Ročník 4 |
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| Hlavní autori: | , , , |
| Médium: | Journal Article |
| Jazyk: | Japanese |
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Informa UK Limited
08.07.2024
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| ISSN: | 2766-0400 |
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| Author | Luca Foppiano Guillaume Lambard Masashi Ishii Toshiyuki Amagasa |
|---|---|
| Author_xml | – sequence: 1 fullname: Luca Foppiano – sequence: 2 orcidid: 0000-0003-0275-4079 fullname: Guillaume Lambard – sequence: 3 fullname: Toshiyuki Amagasa – sequence: 4 fullname: Masashi Ishii |
| BackLink | https://cir.nii.ac.jp/crid/1870303265915764352$$DView record in CiNii |
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| Contributor | National Institute for Materials Science (NIMS) This work was partially supported by the MEXT Programme: Data Creation and Utilisation-Type Material Research and Development Project (Digital Transformation Initiative Centre for Magnetic Materials) Grant Number JPMXP1122715503 Université de Tsukuba = University of Tsukuba |
| Contributor_xml | – sequence: 1 fullname: National Institute for Materials Science (NIMS) – sequence: 2 fullname: Université de Tsukuba = University of Tsukuba – sequence: 3 fullname: This work was partially supported by the MEXT Programme: Data Creation and Utilisation-Type Material Research and Development Project (Digital Transformation Initiative Centre for Magnetic Materials) Grant Number JPMXP1122715503 |
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| DOI | 10.48550/arxiv.2401.11052 10.6084/m9.figshare.26202256.v1 10.1080/27660400.2024.2356506 10.6084/m9.figshare.26202256 |
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| SubjectTerms | [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL] [SCCO.COMP]Cognitive science/Computer science benchmark Computation and Language (cs.CL) Computer Science - Computation and Language evaluation FOS: Computer and information sciences Large language models Machine learning Materials informatics Materials of engineering and construction. Mechanics of materials materials science NER TA401-492 TDM Text and data mining |
| Title | Mining experimental data from materials science literature with large language models: an evaluation study |
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