CAREER: OPENALIGN: Towards Open-World Preference Alignment for Large Language Models — NSF Award to Northwestern University at Chi
As artificial intelligence (AI) systems are increasingly deployed in critical domains such as healthcare, scientific discovery, and autonomous decision-making, ensuring that foundation AI models such as large language models (LLMs) align with human values and preferences has become essential for their safe and benefici
| Award title | CAREER: OPENALIGN: Towards Open-World Preference Alignment for Large Language Models |
|---|---|
| Award ID | 2544599 |
| Awardee | Northwestern University at Chicago |
| City | EVANSTON |
| State | IL |
| Amount obligated | $419,999 |
| Principal investigator | Kaize Ding |
| Program | Info Integration & Informatics |
| Start date | 10/01/2026 |
| Abstract | As artificial intelligence (AI) systems are increasingly deployed in critical domains such as healthcare, scientific discovery, and autonomous decision-making, ensuring that foundation AI models such as large language models (LLMs) align with human values and preferences has become essential for their safe and beneficial deployment. However, most existing approaches rely on large amounts of high-quality labeled preference data and assume clean, stable, well-controlled environments. These assumpt |
| Source | NSF Awards |
$799/mo
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