\mathcal XFT: Unlocking the Power of Code Instruction Tuning by Simply Merging Upcycled Mixture-of-Experts

Yifeng Ding, Jiawei Liu, Yuxiang Wei, Lingming Zhang


Anthology ID:
2024.acl-long.699
Volume:
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
August
Year:
2024
Address:
Bangkok, Thailand
Editors:
Lun-Wei Ku, Andre Martins, Vivek Srikumar
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
12941–12955
Language:
URL:
https://aclanthology.org/2024.acl-long.699
DOI:
Bibkey:
Cite (ACL):
Yifeng Ding, Jiawei Liu, Yuxiang Wei, and Lingming Zhang. 2024. \mathcal XFT: Unlocking the Power of Code Instruction Tuning by Simply Merging Upcycled Mixture-of-Experts. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 12941–12955, Bangkok, Thailand. Association for Computational Linguistics.
Cite (Informal):
\mathcal XFT: Unlocking the Power of Code Instruction Tuning by Simply Merging Upcycled Mixture-of-Experts (Ding et al., ACL 2024)
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PDF:
https://aclanthology.org/2024.acl-long.699.pdf