1 citations · 2 across the 5 of their papers we have counts for
9 papers
CI4A: Semantic Component Interfaces for Agents Empowering Web Automation
Zhi Qiu, Jiazheng Sun, Chenxiao Xia +2
While Large Language Models demonstrate remarkable proficiency in high-level semantic planning, they remain limited in handling fine-grained, low-level web component manipulations.…
Generating High-Quality Datasets for Code Editing via Open-Source Language Models
Zekai Zhang, Mingwei Liu, Zhenxi Chen +7
Code editing plays a vital role in software engineering, requiring developers to adjust existing code according to natural language instructions while keeping functionality intact…
EvolMathEval: Towards Evolvable Benchmarks for Mathematical Reasoning via Evolutionary Testing
Shengbo Wang, Mingwei Liu, Zike Li +4
The rapid advancement of Large Language Models (LLMs) poses a significant challenge to existing mathematical reasoning benchmarks. However, these benchmarks tend to become easier o…
A Hierarchical and Evolvable Benchmark for Fine-Grained Code Instruction Following with Multi-Turn Feedback
Guoliang Duan, Mingwei Liu, Yanlin Wang +3
Large language models (LLMs) have advanced significantly in code generation, yet their ability to follow complex programming instructions with layered and diverse constraints remai…
Code Copycat Conundrum: Demystifying Repetition in LLM-based Code Generation
Mingwei Liu, Juntao Li, Ying Wang +9
Despite recent advances in Large Language Models (LLMs) for code generation, the quality of LLM-generated code still faces significant challenges. One significant issue is code rep…
RustEvo^2: An Evolving Benchmark for API Evolution in LLM-based Rust Code Generation
Linxi Liang, Jing Gong, Mingwei Liu +5
Large Language Models (LLMs) have become pivotal tools for automating code generation in software development. However, these models face significant challenges in producing versio…