3 citations · 5 across the 3 of their papers we have counts for
3 papers
cs.AI2026
UXBench: Measuring the Actionability of LLM-Generated UX Critiques
Wenjie Wang, Yue Huang, Zipeng Ling +11
Large language models (LLMs) are increasingly deployed as UX judges that inspect interfaces, diagnose usability problems, and propose repairs. Yet no controlled benchmark measures…
cs.CL2023★ 2 cited
Bridging Code Semantic and LLMs: Semantic Chain-of-Thought Prompting for Code Generation
Yingwei Ma, Yue Yu, Shanshan Li +5
Large language models (LLMs) have showcased remarkable prowess in code generation. However, automated code generation is still challenging since it requires a high-level semantic m…
cs.CL2023★ 3 cited
At Which Training Stage Does Code Data Help LLMs Reasoning?
Yingwei Ma, Yue Liu, Yue Yu +4
Large Language Models (LLMs) have exhibited remarkable reasoning capabilities and become the foundation of language technologies. Inspired by the great success of code data in trai…