Training Generative Question-Answering on Synthetic Data Obtained from an Instruct-tuned Model

Kosuke Takahashi, Takahiro Omi, Kosuke Arima, Tatsuya Ishigaki


Anthology ID:
2023.paclic-1.78
Volume:
Proceedings of the 37th Pacific Asia Conference on Language, Information and Computation
Month:
December
Year:
2023
Address:
Hong Kong, China
Editors:
Chu-Ren Huang, Yasunari Harada, Jong-Bok Kim, Si Chen, Yu-Yin Hsu, Emmanuele Chersoni, Pranav A, Winnie Huiheng Zeng, Bo Peng, Yuxi Li, Junlin Li
Venue:
PACLIC
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
786–791
Language:
URL:
https://aclanthology.org/2023.paclic-1.78
DOI:
Bibkey:
Cite (ACL):
Kosuke Takahashi, Takahiro Omi, Kosuke Arima, and Tatsuya Ishigaki. 2023. Training Generative Question-Answering on Synthetic Data Obtained from an Instruct-tuned Model. In Proceedings of the 37th Pacific Asia Conference on Language, Information and Computation, pages 786–791, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Training Generative Question-Answering on Synthetic Data Obtained from an Instruct-tuned Model (Takahashi et al., PACLIC 2023)
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PDF:
https://preview.aclanthology.org/nschneid-patch-1/2023.paclic-1.78.pdf