Large language models ensemble for biochemical properties detection in scientific articles (2025)
- Authors:
- USP affiliated authors: SILVA, DIEGO FURTADO - ICMC ; MARCACINI, RICARDO MARCONDES - ICMC ; GÔLO, MARCOS PAULO SILVA - ICMC ; MEDEIROS JÚNIOR, JOSÉ GILBERTO BARBOSA DE - ICMC ; BOAS, GABRIELE SOUZA VILAS - ICMC ; LOBATO, FÁBIO MANOEL FRANÇA - ICMC
- Unidade: ICMC
- Subjects: PROCESSAMENTO DE LINGUAGEM NATURAL; MINERAÇÃO DE DADOS; DESCOBERTA DE CONHECIMENTO; APRENDIZADO COMPUTACIONAL; FÁRMACOS; PRODUTOS NATURAIS
- Keywords: Chemistry Text Mining; Knowledge Graphs; NatUKE Benchmark; LLM Fine-tuning; Few-shot Learning
- Agências de fomento:
- Language: Inglês
- Sustainable Development Goals (GDS):
03. Good health and well-being
09. Industry, innovation and infrastructure
- Imprenta:
- Source:
- Título: Proceedings
- ISSN: 1613-0073
- Conference titles: Extended Semantic Web Conference - ESWC
-
ABNT
GÔLO, Marcos Paulo Silva et al. Large language models ensemble for biochemical properties detection in scientific articles. 2025, Anais.. Aachen: CEUR-WS, 2025. Disponível em: https://ceur-ws.org/Vol-4020/BiKE_Paper_ID_1.pdf. Acesso em: 24 dez. 2025. -
APA
Gôlo, M. P. S., Medeiros Júnior, J. G. B. de, Vilas Boas, G. S., Lobato, F. M. F., Silva, D. F., & Marcacini, R. M. (2025). Large language models ensemble for biochemical properties detection in scientific articles. In Proceedings. Aachen: CEUR-WS. Recuperado de https://ceur-ws.org/Vol-4020/BiKE_Paper_ID_1.pdf -
NLM
Gôlo MPS, Medeiros Júnior JGB de, Vilas Boas GS, Lobato FMF, Silva DF, Marcacini RM. Large language models ensemble for biochemical properties detection in scientific articles [Internet]. Proceedings. 2025 ;[citado 2025 dez. 24 ] Available from: https://ceur-ws.org/Vol-4020/BiKE_Paper_ID_1.pdf -
Vancouver
Gôlo MPS, Medeiros Júnior JGB de, Vilas Boas GS, Lobato FMF, Silva DF, Marcacini RM. Large language models ensemble for biochemical properties detection in scientific articles [Internet]. Proceedings. 2025 ;[citado 2025 dez. 24 ] Available from: https://ceur-ws.org/Vol-4020/BiKE_Paper_ID_1.pdf - One-class graph autoencoder: a new end-to-end, low-dimensional, and interpretable approach for node classfication
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