Collaborative Research: III: Medium: Advancing Large Language Model Unlearning: Foundation — NSF Award to Michigan State Universit
Large language models (LLMs) are increasingly integrated into daily life, powering applications in education, healthcare, code generation, and more. However, these models can inadvertently memorize and reproduce sensitive or harmful content, including private user data, copyrighted material, and unsafe instructions. Re
| Award title | Collaborative Research: III: Medium: Advancing Large Language Model Unlearning: Foundation |
|---|---|
| Award ID | 2504263 |
| Awardee | Michigan State University |
| City | EAST LANSING |
| State | MI |
| Amount obligated | $268,000 |
| Principal investigator | Sijia Liu |
| Program | Info Integration & Informatics |
| Start date | 10/01/2025 |
| Abstract | Large language models (LLMs) are increasingly integrated into daily life, powering applications in education, healthcare, code generation, and more. However, these models can inadvertently memorize and reproduce sensitive or harmful content, including private user data, copyrighted material, and unsafe instructions. Retraining LLMs from scratch to remove such content is often impractical due to high cost and complexity. This project charts a new course toward more controllable, debuggable, and s |
| Source | NSF Awards |
$799/mo
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