most citedMFTCoder: Boosting Code LLMs with Multitask Fine-Tuning

1 citations · 1 across the 3 of their papers we have counts for

collaborators
Showing cs.LGShow all

2 papers · 1 filter

cs.LG2026

Domain-Skewed Federated Learning with Feature Decoupling and Calibration

Huan Wang, Jun Shen, Jun Yan +1

Federated learning (FL) allows distributed clients to collaboratively train a global model in a privacy-preserving manner. However, one major challenge is domain skew, where client…

cs.LG2023★ 1 cited

MFTCoder: Boosting Code LLMs with Multitask Fine-Tuning

Bingchang Liu, Chaoyu Chen, Cong Liao +9

Code LLMs have emerged as a specialized research field, with remarkable studies dedicated to enhancing model's coding capabilities through fine-tuning on pre-trained models. Previo…