9 citations · 16 across the 9 of their papers we have counts for
10 papers
Seeing the Whole Elephant: A Benchmark for Failure Attribution in LLM-based Multi-Agent Systems
Mengzhuo Chen, Junjie Wang, Fangwen Mu +4
Failure attribution, i.e., identifying the responsible agent and decisive step of a failure, is particularly challenging in LLM-based multi-agent systems (MAS) due to their natural…
EXPEREPAIR: Dual-Memory Enhanced LLM-based Repository-Level Program Repair
Fangwen Mu, Junjie Wang, Lin Shi +3
Automatically repairing software issues remains a fundamental challenge at the intersection of software engineering and AI. Although recent advances in Large Language Models (LLMs)…
Vulnerability of Text-to-Image Models to Prompt Template Stealing: A Differential Evolution Approach
Yurong Wu, Fangwen Mu, Qiuhong Zhang +8
Prompt trading has emerged as a significant intellectual property concern in recent years, where vendors entice users by showcasing sample images before selling prompt templates th…
CodePurify: Defend Backdoor Attacks on Neural Code Models via Entropy-based Purification
Fangwen Mu, Junjie Wang, Zhuohao Yu +4
Neural code models have found widespread success in tasks pertaining to code intelligence, yet they are vulnerable to backdoor attacks, where an adversary can manipulate the victim…
ClarifyGPT: Empowering LLM-based Code Generation with Intention Clarification
Fangwen Mu, Lin Shi, Song Wang +5
We introduce a novel framework named ClarifyGPT, which aims to enhance code generation by empowering LLMs with the ability to identify ambiguous requirements and ask targeted clari…
Developer-Intent Driven Code Comment Generation
Fangwen Mu, Xiao Chen, Lin Shi +2
Existing automatic code comment generators mainly focus on producing a general description of functionality for a given code snippet without considering developer intentions. Howev…