3 citations · 4 across the 4 of their papers we have counts for
Showing 2026Show all
2 papers · 1 filter
cs.LG2026
CAARL: In-Context Learning for Interpretable Co-Evolving Time Series Forecasting
Etienne Tajeuna, Patrick Asante Owusu, Armelle Brun +1
In this paper we investigate forecasting coevolving time series that feature intricate dependencies and nonstationary dynamics by using an LLM Large Language Models approach We pro…
cs.AI2026
Supervised Fine-Tuning versus Reinforcement Learning: A Study of Post-Training Methods for Large Language Models
Haitao Jiang, Wenbo Zhang, Jiarui Yao +3
Pre-trained Large Language Model (LLM) exhibits broad capabilities, yet, for specific tasks or domains their attainment of higher accuracy and more reliable reasoning generally dep…