10 papers
GAUSS: Benchmarking Structured Mathematical Skills for Large Language Models
Yue Zhang, Jiaxin Zhang, Qiuyu Ren +5
We introduce \textbf{GAUSS} (\textbf{G}eneral \textbf{A}ssessment of \textbf{U}nderlying \textbf{S}tructured \textbf{S}kills in Mathematics), a benchmark that evaluates LLMs' mathe…
A Survey of Automatic Prompt Optimization with Instruction-focused Heuristic-based Search Algorithm
Wendi Cui, Zhuohang Li, Hao Sun +5
Recent advances in Large Language Models have led to remarkable achievements across a variety of Natural Language Processing tasks, making prompt engineering increasingly central t…
SEE: Strategic Exploration and Exploitation for Cohesive In-Context Prompt Optimization
Wendi Cui, Zhuohang Li, Hao Sun +5
Designing optimal prompts for Large Language Models (LLMs) is a complicated and resource-intensive task, often requiring substantial human expertise and effort. Existing approaches…
Language Models for Materials Discovery and Sustainability: Progress, Challenges, and Opportunities
Zongrui Pei, Junqi Yin, Jiaxin Zhang
Significant advancements have been made in one of the most critical branches of artificial intelligence: natural language processing (NLP). These advancements are exemplified by th…
SCE: Scalable Consistency Ensembles Make Blackbox Large Language Model Generation More Reliable
Jiaxin Zhang, Zhuohang Li, Wendi Cui +3
Large language models (LLMs) have demonstrated remarkable performance, yet their diverse strengths and weaknesses prevent any single LLM from achieving dominance across all tasks.…
Towards Statistical Factuality Guarantee for Large Vision-Language Models
Zhuohang Li, Chao Yan, Nicholas J. Jackson +4
Advancements in Large Vision-Language Models (LVLMs) have demonstrated promising performance in a variety of vision-language tasks involving image-conditioned free-form text genera…