CAREER: Structure-Aware Learning from Weak Supervision for Knowledge Acquisition — NSF Award to University of Virginia Main Campus
Knowledge acquisition—the ability of artificial intelligence (AI) systems to extract actionable insights from vast amounts of unstructured text—is critical for advancements in healthcare, education, and scientific discovery. While Large Language Models (LLMs) have shown impressive capabilities, their reliability depend
| Award title | CAREER: Structure-Aware Learning from Weak Supervision for Knowledge Acquisition |
|---|---|
| Award ID | 2541536 |
| Awardee | University of Virginia Main Campus |
| City | CHARLOTTESVILLE |
| State | VA |
| Amount obligated | $538,798 |
| Principal investigator | Yu Meng |
| Program | Info Integration & Informatics |
| Start date | 10/01/2026 |
| Abstract | Knowledge acquisition—the ability of artificial intelligence (AI) systems to extract actionable insights from vast amounts of unstructured text—is critical for advancements in healthcare, education, and scientific discovery. While Large Language Models (LLMs) have shown impressive capabilities, their reliability depends heavily on massive, perfectly curated datasets, which are expensive and often unavailable in specialized domains. This CAREER project addresses this bottleneck by developing a ne |
| Source | NSF Awards |
$799/mo
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