1 citations · 1 across the 5 of their papers we have counts for
5 papers
Lita: Light Agent Uncovers the Agentic Coding Capabilities of LLMs
Hankun Dai, Maoquan Wang, Mengnan Qi +6
Large language models (LLMs) are increasingly being applied to programming tasks, ranging from single-turn code completion to autonomous agents. Current code agent designs frequent…
Is Next Token Prediction Sufficient for GPT? Exploration on Code Logic Comprehension
Mengnan Qi, Yufan Huang, Yongqiang Yao +3
Large language models (LLMs) has experienced exponential growth, they demonstrate remarkable performance across various tasks. Notwithstanding, contemporary research primarily cent…
Rethinking the Instruction Quality: LIFT is What You Need
Yang Xu, Yongqiang Yao, Yufan Huang +4
Instruction tuning, a specialized technique to enhance large language model (LLM) performance via instruction datasets, relies heavily on the quality of employed data. Existing qua…
SUT: Active Defects Probing for Transcompiler Models
Mengnan Qi, Yufan Huang, Maoquan Wang +5
Automatic Program translation has enormous application value and hence has been attracting significant interest from AI researchers. However, we observe that current program transl…
Program Translation via Code Distillation
Yufan Huang, Mengnan Qi, Yongqiang Yao +4
Software version migration and program translation are an important and costly part of the lifecycle of large codebases. Traditional machine translation relies on parallel corpora…