This $347,570 Project Grant, awarded by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070), supports research at Emory University to develop a comprehensive framework for knowledge graph-large language model (KG-LLM) co-learning in healthcare. The key objectives are to: 1) create novel methods for constructing comprehensive healthcare knowledge graphs by unifying existing sources, continuously improving them, and aligning them...
This Project Grant award of $300,000 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program supports research at Cornell University to develop improved evaluation concepts and technologies for large language models (LLMs). The project aims to discover a taxonomy of "evaluation concepts" that can assess LLM responses for criteria like factuality, level of detail, and alignment with user needs. It will then develop customized reward...
This $317,591 federal Project Grant awarded by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) will fund research to enhance personalized healthcare through the use of large language models (LLMs) and novel memory semiconductor devices. The project aims to develop efficient retrieval-augmented generation (RAG) techniques for LLM personalization, focusing on reducing latency and hardware overhead through algorithm-hardware...
This Project Grant award of $360,000.00 from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) to the Massachusetts Institute of Technology (MIT) aims to develop new methods for efficient, architecture-aware algorithms for large language models (LLMs). The goals are to make existing LLM applications more efficient, enable new applications, and broaden access to this transformative AI technology. The key focus areas include: (1)...
This $100,000 Project Grant was awarded by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070). The grant supports the development of an open, community-driven evaluation infrastructure to assess the safety risks of large language models (LLMs). The project aims to: (i) conduct surveys and interviews with experts to identify critical safety concerns and evaluation gaps; (ii) organize a workshop to refine the evaluation...
This $300,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program supports research to improve the evaluation and functionality of large language models (LLMs). The University of Texas at Austin will lead this 3-year collaborative research project to identify a taxonomy of "evaluation concepts" for assessing LLM responses and develop technology to automatically evaluate and improve LLM performance based on...
This Project Grant award, provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program, aims to understand the effects of large language models (LLMs) on the work of online information professionals. The $370,692 award to the University of Washington will fund research to develop an epistemological framework for characterizing the risks posed by LLM-generated content, as well as proactive and reactive approaches to assessing...
This $763,741 project grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program aims to better align how human and artificial intelligence (AI) models process language. The researchers at New York University will explore techniques to modify AI architectures, such as adopting semantic training objectives and leveraging causal intervention methods, to bring them more in line with how humans derive meaning from sentences and handle...
This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Project Grant award, with a funding amount of $200,000, will support the development of concept-based reasoning approaches to improve interpretability and accountability of deep neural network (DNN) models for healthcare applications. The key research tasks under this 2-year award (10/1/2025 - 9/30/2027) are: (1) building inherently explainable concept-based DNN models for medical diagnosis, and (2)...
This Project Grant award of $300,000 from the National Science Foundation (NSF) Technology, Innovation, and Partnerships (CFDA 47.084) program supports a study at Georgetown University to develop an evaluation methodology for measuring the impacts of implementing large language model (LLM)-based tools to assist human experts working in federal, state, and local government programs. The project will compare the performance of LLM-only, human-only, and human-LLM hybrid responses across key metrics...