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Mengliang He

3 accepted papers

2025

Flow2Code: Evaluating Large Language Models for Flowchart-based Code Generation Capability

ACL 2025finding

While large language models (LLMs) show promise in code generation, existing benchmarks neglect the flowchart-based code generation. To promote further research on flowchart-based code generation, this work presents Flow2Code, a novel benchmark for flowchart-based code generation evaluation. The eva…

2025

Mis-prompt: Benchmarking Large Language Models for Proactive Error Handling

ACL 2025long

Large language models (LLMs) have demonstrated significant advancements in error handling. Current error-handling works are performed in a passive manner, with explicit error-handling instructions. However, in real-world scenarios, explicit error-handling instructions are usually unavailable. In thi…

Cited by 0SourcePDFScholar
2025

RepoDebug: Repository-Level Multi-Task and Multi-Language Debugging Evaluation of Large Language Models

EMNLP 2025

Large Language Models (LLMs) have exhibited significant proficiency in code debugging, especially in automatic program repair, which may substantially reduce the time consumption of developers and enhance their efficiency. Significant advancements in debugging datasets have been made to promote the