11 citations · 16 across the 20 of their papers we have counts for
10 papers · 1 filter
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…
Neuron-Guided Interpretation of Code LLMs: Where, Why, and How?
Zhe Yin, Xiaodong Gu, Beijun Shen
Code language models excel on code intelligence tasks, yet their internal interpretability is underexplored. Existing neuron interpretability techniques from NLP are suboptimal for…
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…
ALT4Decompile: Inferring C-aligned Abstract Loop Tree for LLM-Based Binary Decompilation
Yongpan Wang, Puzhuo Liu, Xin Xu +4
Decompilation refers to the process of recovering high-level (C) language code from low-level (assembly) code. Recent Large Language Model (LLM)-based methods can generate re-execu…
SWE-QA: Can Language Models Answer Repository-level Code Questions?
Weihan Peng, Yuling Shi, Yuhang Wang +3
Understanding and reasoning about entire software repositories is an essential capability for intelligent software engineering tools. While existing benchmarks such as CoSQA and Co…
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…
Empowering AI to Generate Better AI Code: Guided Generation of Deep Learning Projects with LLMs
Chen Xie, Mingsheng Jiao, Xiaodong Gu +1
While large language models (LLMs) have been widely applied to code generation, they struggle with generating entire deep learning projects, which are characterized by complex stru…
APIRAT: Integrating Multi-source API Knowledge for Enhanced Code Translation with LLMs
Chaofan Wang, Guanjie Qiu, Xiaodong Gu +1
Code translation is an essential task in software migration, multilingual development, and system refactoring. Recent advancements in large language models (LLMs) have demonstrated…