2 papers
cs.CL2024
CodeACT: Code Adaptive Compute-efficient Tuning Framework for Code LLMs
Weijie Lv, Xuan Xia, Sheng-Jun Huang
Large language models (LLMs) have shown great potential in code-related tasks, yet open-source models lag behind their closed-source counterparts. To bridge this performance gap, e…
cs.LG2024
One-shot Active Learning Based on Lewis Weight Sampling for Multiple Deep Models
Sheng-Jun Huang, Yi Li, Yiming Sun +1
Active learning (AL) for multiple target models aims to reduce labeled data querying while effectively training multiple models concurrently. Existing AL algorithms often rely on i…