Collaborative Research: RI: Building Knowledgeable, Reliable, and Proactive Language Model — NSF Award to University of Washington
Large language models (LLM) are increasingly used by the public to seek health information, but current LLM-based systems can still generate inaccurate information due to the well-known problem of LLM hallucinations, while expressing it with high confidence. The issue of confidently representing erroneous data creates
| Award title | Collaborative Research: RI: Building Knowledgeable, Reliable, and Proactive Language Model |
|---|---|
| Award ID | 2554007 |
| Awardee | University of Washington |
| City | SEATTLE |
| State | WA |
| Amount obligated | $600,000 |
| Principal investigator | Yulia Tsvetkov |
| Program | Robust Intelligence |
| Start date | 09/15/2026 |
| Abstract | Large language models (LLM) are increasingly used by the public to seek health information, but current LLM-based systems can still generate inaccurate information due to the well-known problem of LLM hallucinations, while expressing it with high confidence. The issue of confidently representing erroneous data creates risks in high-stakes settings. This project addresses that problem by developing artificial intelligence methods that reduce hallucinations and improve the reliability, transparenc |
| Source | NSF Awards |
$799/mo
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