2 papers
cs.LG2024
LeMoLE: LLM-Enhanced Mixture of Linear Experts for Time Series Forecasting
Lingzheng Zhang, Lifeng Shen, Yimin Zheng +3
Recent research has shown that large language models (LLMs) can be effectively used for real-world time series forecasting due to their strong natural language understanding capabi…
cs.AI2024
MCCoder: Streamlining Motion Control with LLM-Assisted Code Generation and Rigorous Verification
Yin Li, Liangwei Wang, Shiyuan Piao +4
Large Language Models (LLMs) have demonstrated significant potential in code generation. However, in the factory automation sector, particularly motion control, manual programming,…