53 citations · 74 across the 5 of their papers we have counts for
6 papers
Beyond Code Pairs: Dialogue-Based Data Generation for LLM Code Translation
Le Chen, Nuo Xu, Winson Chen +7
Large language models (LLMs) have shown remarkable capabilities in code translation, yet their performance deteriorates in low-resource programming domains such as Fortran and emer…
\textsc{GUI-Spotlight}: Adaptive Iterative Focus Refinement for Enhanced GUI Visual Grounding
Bin Lei, Nuo Xu, Ali Payani +4
Multimodal large language models (MLLMs) have markedly expanded the competence of graphical user-interface (GUI) systems, propelling them beyond controlled simulations into complex…
Fortran2CPP: Automating Fortran-to-C++ Translation using LLMs via Multi-Turn Dialogue and Dual-Agent Integration
Le Chen, Bin Lei, Dunzhi Zhou +4
Translating legacy Fortran code into C++ is a crucial step in modernizing high-performance computing (HPC) applications. However, the scarcity of high-quality, parallel Fortran-to-…
HPC-GPT: Integrating Large Language Model for High-Performance Computing
Xianzhong Ding, Le Chen, Murali Emani +6
Large Language Models (LLMs), including the LLaMA model, have exhibited their efficacy across various general-domain natural language processing (NLP) tasks. However, their perform…
Data Race Detection Using Large Language Models
Le Chen, Xianzhong Ding, Murali Emani +3
Large language models (LLMs) are demonstrating significant promise as an alternate strategy to facilitate analyses and optimizations of high-performance computing programs, circumv…
Creating a Dataset for High-Performance Computing Code Translation using LLMs: A Bridge Between OpenMP Fortran and C++
Bin Lei, Caiwen Ding, Le Chen +2
In this study, we present a novel dataset for training machine learning models translating between OpenMP Fortran and C++ code. To ensure reliability and applicability, the dataset…