3 citations · 6 across the 16 of their papers we have counts for
Showing cs.CLShow all
2 papers · 1 filter
cs.CL2026
Full-Stack Domain Enhancement for Combustion LLMs: Construction and Optimization
Quanjia Xiao, Weimin Ouyang, Zonglin Yang +4
Large language models (LLMs) in the direction of task adaptation and capability enhancement for professional fields demonstrate significant application potential. Nevertheless, for…
cs.CL2026
A unified foundational framework for knowledge injection and evaluation of Large Language Models in Combustion Science
Zonglin Yang, Runze Mao, Tianhao Wu +3
To advance foundation Large Language Models (LLMs) for combustion science, this study presents the first end-to-end framework for developing domain-specialized models for the combu…