CAREER: From Learning to Forgetting: Linguistic Complexity, Learning Dynamics, and Robustn — NSF Award to University of Massachuse
Artificial intelligence systems that generate text now influence how people search for information, learn new skills, obtain health advice, and communicate online. Yet these systems can behave in ways that are hard to predict. They can copy misleading patterns from their training data, produce text that is too complex
| Award title | CAREER: From Learning to Forgetting: Linguistic Complexity, Learning Dynamics, and Robustn |
|---|---|
| Award ID | 2541273 |
| Awardee | University of Massachusetts Lowell |
| City | LOWELL |
| State | MA |
| Amount obligated | $341,598 |
| Principal investigator | Hadi Amiri |
| Program | Info Integration & Informatics |
| Start date | 07/01/2026 |
| Abstract | Artificial intelligence systems that generate text now influence how people search for information, learn new skills, obtain health advice, and communicate online. Yet these systems can behave in ways that are hard to predict. They can copy misleading patterns from their training data, produce text that is too complex for a reader's needs, and retain private, outdated, or incorrect content. This project studies how these systems learn, keep, and forget language patterns over time, and uses that |
| Source | NSF Awards |
$799/mo
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