4 papers · 1 filter
Knowledge Matters: Injecting Project and Testing Knowledge into LLM-based Unit Test Generation
Anji Li, Mingwei Liu, Zhenxi Chen +5
Automated unit test generation using large language models (LLMs) holds great promise but often struggles with generating tests that are both correct and maintainable in real-world…
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…
AdaDec: A Uncertainty-Guided Lookahead Decoding Framework for LLM-Based Code Generation
Kaifeng He, Mingwei Liu, Chong Wang +4
Code generation with large language models (LLMs) is highly sensitive to token selection during decoding, particularly at uncertain decision points that influence program logic. Wh…
A Preliminary Study on the Robustness of Code Generation by Large Language Models
Zike Li, Mingwei Liu, Anji Li +4
Robustness is a critical factor for reliable code generation by large language models, yet most evaluations focus on correctness and overlook key issues such as missing input valid…