1 citations · 1 across the 7 of their papers we have counts for
8 papers
Close the Loop: Synthesizing Infinite Tool-Use Data via Multi-Agent Role-Playing
Yuwen Li, Wei Zhang, Zelong Huang +8
Enabling Large Language Models (LLMs) to reliably invoke external tools remains a critical bottleneck for autonomous agents. Existing approaches suffer from three fundamental chall…
AGRO-SQL: Agentic Group-Relative Optimization with High-Fidelity Data Synthesis
Cehua Yang, Dongyu Xiao, Junming Lin +7
The advancement of Text-to-SQL systems is currently hindered by the scarcity of high-quality training data and the limited reasoning capabilities of models in complex scenarios. In…
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…
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…
UCoder: Unsupervised Code Generation by Internal Probing of Large Language Models
Jiajun Wu, Jian Yang, Wei Zhang +6
Large language models (LLMs) have demonstrated remarkable capabilities in code generation tasks. However, their effectiveness heavily relies on supervised training with extensive l…
Scaling Laws for Code: Every Programming Language Matters
Jian Yang, Shawn Guo, Lin Jing +8
Code large language models (Code LLMs) are powerful but costly to train, with scaling laws predicting performance from model size, data, and compute. However, different programming…