CAREER: Frontiers of Knowledge in Foundation Models

Foundation models are large artificial intelligence (AI) systems trained on vast amounts of data to perform a wide range of tasks, including answering questions, generating content, and assisting decision-making. These models are increasingly used in areas that affect everyday life, such as healthcare, education, and environmental planning. However, despite their impressive capabilities, they often rely on patterns and correlations in data rather than true causal relationships. This limitation can lead to unreliable or misleading outputs, especially in high-stakes situations. For example, a model may incorrectly assume that one factor causes another simply because they frequently appear together in data. This project addresses this critical challenge by enabling foundation models to better understand and use causal knowledge, which describes how one factor directly influences another in the real world. By improving the ability of these models to reason about cause and effect, the project aims to make them more reliable, transparent, and aligned with human reasoning. The results will support safer and more effective use of AI in important societal domains, strengthen decision-making in complex environments, and contribute to education and workforce development by training students in emerging areas of trustworthy AI. This project develops a systematic framework for understanding, leveraging, editing, and applying causal knowledge in foundation models, organized into four complementary thrusts. The first thrust introduces methods to interpret causal relationships embedded within large-scale models by analyzing internal components that encode causal knowledge across language, vision, and multimodal systems. The second thrust designs approaches to incorporate external causal knowledge into model reasoning, improving performance in tasks such as question answering, causal reasoning, and video understanding. The third thrust establishes techniques for editing causal knowledge within models, enabling targeted updates to specific relationships while preserving overall model performance and consistency. The fourth thrust focuses on empirical evaluation and application of the proposed methods across diverse application domains, including healthcare, materials science, and environmental systems. The project integrates research with education through curriculum development, student mentoring, and outreach activities, and produces open-source tools and resources to support broader adoption. Together, these efforts advance the development of interpretable, controllable, and generalizable foundation models grounded in a causal perspective. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. NSF Award ID: 2540397 | Program: 01002627DB NSF RESEARCH & RELATED ACTIVIT,01002930DB NSF RESEARCH & RELATED ACTIVIT,01003031DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Jing Ma | Institution: Case Western Reserve University, CLEVELAND, OH | Award Amount: $420,000 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2540397 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2540397.html