59 citations · 102 across the 13 of their papers we have counts for
11 papers · 1 filter
Self-Spec Verifiable Code Generation
Jiaru Qian, Yihong Dong, Yongmin Li +3
Large language models (LLMs) may generate unreliable code on corner cases missed by testing, while formal verification can provide machine-checkable guarantees. Recently, researche…
ClarifyCodeBench: Evaluating LLMs on Clarifying Ambiguous Requirements for Code Generation
Zheng Fang, Dongming Jin, Yihong dong +4
Large Language Models have emerged as programming assistants. However, the efficacy of code generation is constrained by the quality of input requirements, which are frequently amb…
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…
AdapTrack: Constrained Decoding without Distorting LLM's Output Intent
Yongmin Li, Jia Li, Ge Li +1
Language model-based code generation and completion tools have been widely adopted, but they may sometimes produce code that does not meet necessary constraints, such as syntactic…
Showing LLM-Generated Code Selectively Based on Confidence of LLMs
Jia Li, Yuqi Zhu, Yongmin Li +2
Large Language Models (LLMs) have shown impressive abilities in code generation, but they may generate erroneous programs. Reading a program takes ten times longer than writing it.…
DevEval: Evaluating Code Generation in Practical Software Projects
Jia Li, Ge Li, Yunfei Zhao +14
How to evaluate Large Language Models (LLMs) in code generation is an open question. Many benchmarks have been proposed but are inconsistent with practical software projects, e.g.,…
EditSum: A Retrieve-and-Edit Framework for Source Code Summarization
Jia Li, Yongmin Li, Ge Li +3
Existing studies show that code summaries help developers understand and maintain source code. Unfortunately, these summaries are often missing or outdated in software projects. Co…
Structured Chain-of-Thought Prompting for Code Generation
Jia Li, Ge Li, Yongmin Li +1
Large Language Models (LLMs) (e.g., ChatGPT) have shown impressive performance in code generation. LLMs take prompts as inputs, and Chain-of-Thought (CoT) prompting is the state-of…
AceCoder: Utilizing Existing Code to Enhance Code Generation
Jia Li, Yunfei Zhao, Yongmin Li +2
Large Language Models (LLMs) have shown great success in code generation. LLMs take as the input a prompt and output the code. A key question is how to make prompts (i.e., Promptin…
SkCoder: A Sketch-based Approach for Automatic Code Generation
Jia Li, Yongmin Li, Ge Li +3
Recently, deep learning techniques have shown great success in automatic code generation. Inspired by the code reuse, some researchers propose copy-based approaches that can copy t…
Retrieve and Refine: Exemplar-based Neural Comment Generation
Bolin Wei, Yongmin Li, Ge Li +2
Code comment generation which aims to automatically generate natural language descriptions for source code, is a crucial task in the field of automatic software development. Tradit…