6 papers
The MiniMax-M2 Series: Mini Activations Unleashing Max Real-World Intelligence
MiniMax, :, Aili Chen +219
We introduce the MiniMax-M2 series, a family of Mixture-of-Experts language models built around the principle that mini activations can unleash maximum real-world intelligence. The…
Contextualized Code Pretraining for Code Generation
Chen Liu, Qingyuan Liang, Hanwen Zhang +3
As code generation becomes increasingly central to improving software development efficiency, modern code models are largely trained and evaluated on code with natural-language des…
TRACE: Evaluating Execution Efficiency of LLM-Based Code Translation
Zhihao Gong, Zeyu Sun, Dong Huang +3
While Large Language Models (LLMs) have substantially improved the functional correctness of code translation, the critical dimension of \textit{execution efficiency} remains overl…
TRACE: Evaluating Execution Efficiency of LLM-Based Code Translation
Zhihao Gong, Zeyu Sun, Dong Huang +3
While Large Language Models (LLMs) have substantially improved the functional correctness of code translation, the critical dimension of \textit{execution efficiency} remains overl…
DSCodeBench: A Realistic Benchmark for Data Science Code Generation
Shuyin Ouyang, Dong Huang, Jingwen Guo +3
We introduce DSCodeBench, a new benchmark designed to evaluate large language models (LLMs) on complicated and realistic data science code generation tasks. DSCodeBench consists of…
Knowledge-Enhanced Program Repair for Data Science Code
Shuyin Ouyang, Jie M. Zhang, Zeyu Sun +1
This paper introduces DSrepair, a knowledge-enhanced program repair method designed to repair the buggy code generated by LLMs in the data science domain. DSrepair uses knowledge g…