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://preview.aclanthology.org/remove-affiliations/2024.emnlp-industry.61/
- DOI:
- 10.18653/v1/2024.emnlp-industry.61
- 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)
- PDF:
- https://preview.aclanthology.org/remove-affiliations/2024.emnlp-industry.61.pdf