Intelligent Predictive Maintenance RAG framework for Power Plants: Enhancing QA with StyleDFS and Domain Specific Instruction Tuning

Seongtae Hong, Joong Min Shin, Jaehyung Seo, Taemin Lee, Jeongbae Park, Cho Man Young, Byeongho Choi, Heuiseok Lim


Anthology ID:
2024.emnlp-industry.61
Volume:
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track
Month:
November
Year:
2024
Address:
Miami, Florida, US
Editors:
Franck Dernoncourt, Daniel Preoţiuc-Pietro, Anastasia Shimorina
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
805–820
Language:
URL:
https://aclanthology.org/2024.emnlp-industry.61
DOI:
Bibkey:
Cite (ACL):
Seongtae Hong, Joong Min Shin, Jaehyung Seo, Taemin Lee, Jeongbae Park, Cho Man Young, Byeongho Choi, and Heuiseok Lim. 2024. Intelligent Predictive Maintenance RAG framework for Power Plants: Enhancing QA with StyleDFS and Domain Specific Instruction Tuning. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 805–820, Miami, Florida, US. Association for Computational Linguistics.
Cite (Informal):
Intelligent Predictive Maintenance RAG framework for Power Plants: Enhancing QA with StyleDFS and Domain Specific Instruction Tuning (Hong et al., EMNLP 2024)
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PDF:
https://aclanthology.org/2024.emnlp-industry.61.pdf