105 citations · 240 across the 14 of their papers we have counts for
5 papers · 1 filter
Boosting Logical Reasoning in Large Language Models through a New Framework: The Graph of Thought
Bin Lei, pei-Hung Lin, Chunhua Liao +1
Recent advancements in large-scale models, such as GPT-4, have showcased remarkable capabilities in addressing standard queries. However, when facing complex problems that require…
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…
LM4HPC: Towards Effective Language Model Application in High-Performance Computing
Le Chen, Pei-Hung Lin, Tristan Vanderbruggen +3
In recent years, language models (LMs), such as GPT-4, have been widely used in multiple domains, including natural language processing, visualization, and so on. However, applying…
Making Machine Learning Datasets and Models FAIR for HPC: A Methodology and Case Study
Pei-Hung Lin, Chunhua Liao, Winson Chen +3
The FAIR Guiding Principles aim to improve the findability, accessibility, interoperability, and reusability of digital content by making them both human and machine actionable. Ho…
Finding Reusable Machine Learning Components to Build Programming Language Processing Pipelines
Patrick Flynn, Tristan Vanderbruggen, Chunhua Liao +3
Programming Language Processing (PLP) using machine learning has made vast improvements in the past few years. Increasingly more people are interested in exploring this promising f…