7 citations · 10 across the 19 of their papers we have counts for
5 papers · 1 filter
Efficient Grammar-Constrained Decoding via Parser Stack Classification
Yongmin Li, Yihong Dong, Jia Li +1
LLMs are widely used to generate structured output like source code or JSON. Grammar-constrained decoding (GCD) can guarantee the syntactic validity of the generated output, by mas…
Evaluating the Formal Reasoning Capabilities of Large Language Models through Chomsky Hierarchy
Yihong Dong, Jianha Xiao, Xue Jiang +7
The formal reasoning capabilities of LLMs are crucial for advancing automated software engineering. However, existing benchmarks for LLMs lack systematic evaluation based on comput…
Learning from Execution: Self-Evolving Memory for Private-Library Code Generation
Mofei Li, Taozhi Chen, Guowei Yang +1
Large Language Models (LLMs) have achieved strong performance on general code generation, but their effectiveness drops sharply in enterprise settings where software development re…
RealBench: A Repo-Level Code Generation Benchmark Aligned with Real-World Software Development Practices
Jia Li, Hongyi Deng, Yiran Zhang +9
Writing code requires significant time and effort in software development. To automate this process, researchers have made substantial progress using Large Language Models (LLMs) f…
KOCO-BENCH: Can Large Language Models Leverage Domain Knowledge in Software Development?
Xue Jiang, Ge Li, Jiaru Qian +12
Large language models (LLMs) excel at general programming but struggle with domain-specific software development, necessitating domain specialization methods for LLMs to learn and…