Project Grant 2346357
Award Date
9/1/24
Completion Date
2/28/26
Dollars Obligated
$100K
Federal Agency
Division of Computer and Network Systems
Federal Grant Program
47.070
Assistance Type
Project Grant
Place of Performance
Williamsburg, VA, USA
Similar Awards
This $300,000 Project Grant award, funded by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program, supports research to develop improved evaluation concepts and methods for assessing and enhancing the performance of large language models (LLMs). The key objectives are to: Identify a taxonomy of "evaluation concepts" to pinpoint areas where LLMs are underperforming, such as lack of detail or format issues. Develop...
This Project Grant award of $174,999 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) will fund a unified, large language model (LLM)-empowered framework to systematically address software performance challenges. The key products and services to be delivered under this 2-year award (05/01/2025 - 04/30/2027) include: Advancing automation in performance testing, issue localization, and optimization for modern software...
This Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) aims to improve the evaluation and functionality of large language models (LLMs). The key products and services to be delivered include: Identifying a concept taxonomy to assess areas where LLMs are failing, such as lacking detail in responses. 2) Developing reward models to automatically evaluate and improve LLM responses according to the identified...
This Project Grant award of $225,000.00 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports collaborative research to improve the code generation capabilities of large language models (LLMs). The project aims to integrate program analysis techniques, such as symbolic execution and Bayesian analysis, to develop metrics for evaluating the quality of LLM-generated code and train a differentiable reward model to...
This Project Grant award, provided by the National Science Foundation (NSF) through the Computer and Information Science and Engineering (CISE) program (CFDA 47.070), supports research to improve the code generation capabilities of large language models (LLMs). The primary goals are to develop novel techniques for evaluating the semantic properties of LLM-generated code, create a differentiable reward model to fine-tune LLMs for high-quality code generation, and leverage program analyses to...
This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) award of $175,000.00 to the College of William and Mary will support research to develop sustainable and cost-effective artificial intelligence methods for software engineering automation using large language models for code (LLMC). The project aims to optimize LLMC training data and apply model compression techniques to improve efficiency without compromising...
The National Science Foundation (NSF) awarded a $329,183 Project Grant to the College of William & Mary under the Computer and Information Science and Engineering (CFDA 47.070) program. The grant, awarded on October 1, 2023, will fund the development of a framework and methodology to enable researchers and software engineers to better interpret the behavior of AI-powered developer tools that leverage neural language models for source code. The project aims to generate global and local...
This Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) aims to understand the effects of large language models (LLMs) on the work of online information professionals. The $370,692 award, which runs from June 2025 to May 2030, will develop models and tools to help these professionals assess and manage the risks posed by LLM-generated content, which can sometimes contain false or misleading information....
This National Science Foundation (NSF) $900,000 "COLLABORATIVE RESEARCH: SHF: MEDIUM: NATURAL LANGUAGE MODELS WITH EXECUTION DATA FOR SOFTWARE TESTING" Project Grant, awarded under the Computer and Information Science and Engineering program (CFDA 47.070), aims to develop natural language processing (NLP) models to simplify the development and maintenance of software tests. Key objectives include test generation, completion, update, and migration across programming languages, targeting...
This Project Grant award from the National Science Foundation (NSF) Division of Computing and Communication Foundations, under CFDA Program #47.070 "Computer and Information Science and Engineering", provides $300,000 to the University of Illinois to develop natural language processing (NLP) models that can simplify the development and maintenance of software tests (NLP4TEST). The key objectives are to create NLP models for tasks such as test generation, completion, update, and...