Project Grant 2433072

Award Date 7/1/25
Completion Date 6/30/28
Dollars Obligated $300K
Federal Grant Program
47.070
Assistance Type
Project Grant
Place of Performance
Ithaca, NY, 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 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 $300,000 Project Grant was awarded by the National Science Foundation (NSF) under the Technology, Innovation, and Partnerships (TIP) program (CFDA 47.084) and is focused on developing an evaluation methodology for implementing large language model (LLM)-based tools to support human experts working in federal, state, and local government programs. The project aims to conduct a study that evaluates the trade-offs of using LLM-based tools, such as their cost, error rates, and impact on human...
This Project Grant award, valued at $348,227.00, was provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program. The award aims to improve the scalability and effectiveness of healthcare-focused large language models (LLMs) by developing methods to evaluate and mitigate issues with incomplete data. The key products and services to be delivered include: An evaluation framework to address factual and faithfulness...
This $100,000 Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CFDA 47.070) program aims to develop a holistic benchmarking infrastructure for evaluating large language models used in software engineering. The key activities include: Conducting surveys and interviews with the software engineering and machine learning research communities to gather requirements and understand barriers in evaluating large language models for code....
The National Science Foundation has awarded a $299,990 Project Grant to Columbia University to develop a prototype system using large language models to assist in the review process for releasing government records. The project aims to determine the additional data and training required to achieve acceptable levels of accuracy in using large language models to identify information that is already in the public domain, in order to streamline the document review process. The iterative system...
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 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 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 National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Program (CFDA 47.070) Project Grant award of $112,818 to the University of California, Santa Barbara (UCSB) aims to improve the understanding, correction, and adaptation of large language models (LLMs) from a knowledge-oriented perspective. The project will focus on four key research thrusts: 1) understanding how external knowledge interacts with LLM behavior, 2) developing methods to enable LLMs to...

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:

  1. 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 concepts. 3) Curating training data to enable LLM models to perform better on the specified concepts. 4) Iteratively improving LLM responses using the developed reward models.

The total award amount is $300,000 and the project period runs from Jul 1, 2025 to Jun 30, 2028. The work will be performed by Cornell University's Office of Sponsored Programs. The project aims to open-source the concept taxonomy and reward models to enable the public to better assess the performance of LLM systems across applications and drive the open-source community to build stronger, more reliable LLM systems.

Generated 7/15/25, 3:57 AM