FINALE : Finance Domain Instruction-Tuning Dataset with High-Quality Rationales via Chain-of-Thought Prompting - ACL Anthology

This is an internal, incomplete preview of a proposed change to the ACL Anthology. For efficiency reasons, we generate only three BibTeX files per volume, and the preview may be incomplete in other ways, or contain mistakes. Do not treat this content as an official publication.

FINALE : Finance Domain Instruction-Tuning Dataset with High-Quality Rationales via Chain-of-Thought Prompting

Sangmin Lee, Suzie Oh, Saeran Park, Guijin Son, Pilsung Kang

Anthology ID:: 2024.finnlp-2.9
Volume:: Proceedings of the Eighth Financial Technology and Natural Language Processing and the 1st Agent AI for Scenario Planning
Month:: 3 August
Year:: 2024
Address:: Jeju, South Korea
Editors:: Chung-Chi Chen, Tatsuya Ishigaki, Hiroya Takamura, Akihiko Murai, Suzuko Nishino, Hen-Hsen Huang, Hsin-Hsi Chen
Venue:: FinNLP
SIG:
Publisher:: -
Note:
Pages:: 89–106
Language:
URL:: https://aclanthology.org/2024.finnlp-2.9
DOI:
Bibkey:
Cite (ACL):: Sangmin Lee, Suzie Oh, Saeran Park, Guijin Son, and Pilsung Kang. 2024. FINALE : Finance Domain Instruction-Tuning Dataset with High-Quality Rationales via Chain-of-Thought Prompting. In Proceedings of the Eighth Financial Technology and Natural Language Processing and the 1st Agent AI for Scenario Planning, pages 89–106, Jeju, South Korea. -.
Cite (Informal):: FINALE : Finance Domain Instruction-Tuning Dataset with High-Quality Rationales via Chain-of-Thought Prompting (Lee et al., FinNLP 2024)
Copy Citation:
PDF:: https://preview.aclanthology.org/nschneid-patch-4/2024.finnlp-2.9.pdf