1 citations · 1 across the 6 of their papers we have counts for
8 papers
CatchAll: Repository-Aware Exception Handling with Knowledge-Guided LLMs
Qingxiao Tao, Xiaodong Gu, Hao Zhong +1
Exception handling is a vital forward error-recovery mechanism in many programming languages, enabling developers to manage runtime anomalies through structured constructs (e.g., t…
Beyond Language Boundaries: Uncovering Programming Language Families for Code Language Models
Shangbo Yun, Xiaodong Gu, Jianghong Huang +1
The rapid proliferation of diverse programming languages presents both opportunities and challenges for developing multilingual code LLMs. While existing techniques often train cod…
LongCodeZip: Compress Long Context for Code Language Models
Yuling Shi, Yichun Qian, Hongyu Zhang +2
Code generation under long contexts is becoming increasingly critical as Large Language Models (LLMs) are required to reason over extensive information in the codebase. While recen…
Transplant Then Regenerate: A New Paradigm for Text Data Augmentation
Guangzhan Wang, Hongyu Zhang, Beijun Shen +1
Data augmentation is a critical technique in deep learning. Traditional methods like Back-translation typically focus on lexical-level rephrasing, which primarily produces variatio…
EvoC2Rust: A Skeleton-guided Framework for Project-Level C-to-Rust Translation
Chaofan Wang, Tingrui Yu, Beijun Shen +6
Translating legacy C codebases to Rust is increasingly demanded for building safety-critical systems. While various approaches have emerged for this task, they face inherent trade-…
Anti-adversarial Learning: Desensitizing Prompts for Large Language Models
Xuan Li, Zhe Yin, Xiaodong Gu +1
With the widespread use of LLMs, preserving privacy in user prompts has become crucial, as prompts risk exposing privacy and sensitive data to the cloud LLMs. Traditional technique…