20 citations · 20 across the 10 of their papers we have counts for
5 papers · 1 filter
M2G-Eval: Enhancing and Evaluating Multi-granularity Multilingual Code Generation
Fanglin Xu, Wei Zhang, Jian Yang +5
The rapid advancement of code large language models (LLMs) has sparked significant research interest in systematically evaluating their code generation capabilities, yet existing b…
RoboSafe: Safeguarding Embodied Agents via Executable Safety Logic
Le Wang, Zonghao Ying, Xiao Yang +7
Embodied agents powered by vision-language models (VLMs) are increasingly capable of executing complex real-world tasks, yet they remain vulnerable to hazardous instructions that m…
CodeSimpleQA: Scaling Factuality in Code Large Language Models
Jian Yang, Wei Zhang, Yizhi Li +8
Large language models (LLMs) have made significant strides in code generation, achieving impressive capabilities in synthesizing code snippets from natural language instructions. H…
CodeChemist: Test-Time Scaling for Low-Resource Code Generation via Functional Knowledge Transfer
Kaixin Wang, Tianlin Li, Xiaoyu Zhang +6
Code Large Language Models (CodeLLMs) have been widely adopted for Natural Language to Programming Language code generation, powering applications with large user bases. Their perf…
Code2Bench: Scaling Source and Rigor for Dynamic Benchmark Construction
Zhe Zhang, Runlin Liu, Aishan Liu +3
The evaluation of code-generating Large Language Models (LLMs) is fundamentally constrained by two intertwined challenges: a reliance on static, easily contaminated problem sources…