CRII: III: Dynamic Prompting and Pruning for Measuring and Controlling Memorization in Tex — NSF Award to College of William and M
Artificial intelligence (AI) models are increasingly used to process large-scale structured data, yet their ability to memorize and regurgitate information presents both opportunities and risks. This project advances science and technology by developing methods to measure and control memorization in text-attributed gra
| Award title | CRII: III: Dynamic Prompting and Pruning for Measuring and Controlling Memorization in Tex |
|---|---|
| Award ID | 2451436 |
| Awardee | College of William and Mary |
| City | WILLIAMSBURG |
| State | VA |
| Amount obligated | $174,983 |
| Principal investigator | Yanfu Zhang |
| Program | Info Integration & Informatics |
| Start date | 08/01/2025 |
| Abstract | Artificial intelligence (AI) models are increasingly used to process large-scale structured data, yet their ability to memorize and regurgitate information presents both opportunities and risks. This project advances science and technology by developing methods to measure and control memorization in text-attributed graphs (TAGs), which are widely used in social networks, citation networks, and biological systems. While memorization in machine learning can enhance recall of frequently used inform |
| Source | NSF Awards |
$799/mo
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