5 papers
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…
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…
Sample Complexity and Representation Ability of Test-time Scaling Paradigms
Baihe Huang, Shanda Li, Tianhao Wu +5
Test-time scaling paradigms have significantly advanced the capabilities of large language models (LLMs) on complex tasks. Despite their empirical success, theoretical understandin…
EmbedLLM: Learning Compact Representations of Large Language Models
Richard Zhuang, Tianhao Wu, Zhaojin Wen +3
With hundreds of thousands of language models available on Huggingface today, efficiently evaluating and utilizing these models across various downstream, tasks has become increasi…
Thinking LLMs: General Instruction Following with Thought Generation
Tianhao Wu, Janice Lan, Weizhe Yuan +3
LLMs are typically trained to answer user questions or follow instructions similarly to how human experts respond. However, in the standard alignment framework they lack the basic…