Zero-Shot Information Extraction with Community-Fine-Tuned Large Language Models From Open-Ended Interview Transcripts

Nazmul Kazi; Indika Kahanda; S. Indu Rupassara; John W. Kindt

doi:10.1109/ICMLA58977.2023.00138

Zero-Shot Information Extraction with Community-Fine-Tuned Large Language Models From Open-Ended Interview Transcripts

Nazmul Kazi, Indika Kahanda, S. Indu Rupassara, John W. Kindt

School of Computing

University of North Florida

Producción científica: Literary contribution

Idioma original	English
Título de la publicación alojada	Proceedings - 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023
Editores	M. Arif Wani, Mihai Boicu, Moamar Sayed-Mouchaweh, Pedro Henriques Abreu, Joao Gama
Páginas	932-937
Número de páginas	6
ISBN (versión digital)	9798350345346
DOI	https://doi.org/10.1109/ICMLA58977.2023.00138
Estado	Published - 2023
Evento	22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023 - Jacksonville, United States Duración: dic 15 2023 → dic 17 2023

Serie de la publicación

Nombre	Proceedings - 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

Conference

Conference	22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023
País/Territorio	United States
Ciudad	Jacksonville
Período	12/15/23 → 12/17/23

ASJC Scopus Subject Areas

Artificial Intelligence
Computer Science Applications
Computer Vision and Pattern Recognition

Acceder al documento

10.1109/ICMLA58977.2023.00138

Otros archivos y enlaces

Citar esto

Kazi, N., Kahanda, I., Rupassara, S. I., & Kindt, J. W. (2023). Zero-Shot Information Extraction with Community-Fine-Tuned Large Language Models From Open-Ended Interview Transcripts. En M. Arif Wani, M. Boicu, M. Sayed-Mouchaweh, P. H. Abreu, & J. Gama (Eds.), Proceedings - 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023 (pp. 932-937). (Proceedings - 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023). https://doi.org/10.1109/ICMLA58977.2023.00138

Zero-Shot Information Extraction with Community-Fine-Tuned Large Language Models From Open-Ended Interview Transcripts. / Kazi, Nazmul; Kahanda, Indika; Rupassara, S. Indu et al.
Proceedings - 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023. ed. / M. Arif Wani; Mihai Boicu; Moamar Sayed-Mouchaweh; Pedro Henriques Abreu; Joao Gama. 2023. p. 932-937 (Proceedings - 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023).

Producción científica: Literary contribution

Kazi, N, Kahanda, I, Rupassara, SI & Kindt, JW 2023, Zero-Shot Information Extraction with Community-Fine-Tuned Large Language Models From Open-Ended Interview Transcripts. En M Arif Wani, M Boicu, M Sayed-Mouchaweh, PH Abreu & J Gama (eds.), Proceedings - 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023. Proceedings - 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023, pp. 932-937, 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023, Jacksonville, United States, 12/15/23. https://doi.org/10.1109/ICMLA58977.2023.00138

Kazi N, Kahanda I, Rupassara SI, Kindt JW. Zero-Shot Information Extraction with Community-Fine-Tuned Large Language Models From Open-Ended Interview Transcripts. En Arif Wani M, Boicu M, Sayed-Mouchaweh M, Abreu PH, Gama J, editores, Proceedings - 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023. 2023. p. 932-937. (Proceedings - 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023). doi: 10.1109/ICMLA58977.2023.00138

Kazi, Nazmul ; Kahanda, Indika ; Rupassara, S. Indu et al. / Zero-Shot Information Extraction with Community-Fine-Tuned Large Language Models From Open-Ended Interview Transcripts. Proceedings - 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023. editor / M. Arif Wani ; Mihai Boicu ; Moamar Sayed-Mouchaweh ; Pedro Henriques Abreu ; Joao Gama. 2023. pp. 932-937 (Proceedings - 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023).

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