# RI: Small: Controlling Generative Models

> **NSF 01002526DB NSF RESEARCH & RELATED ACTIVIT** · Cornell University (NY) · $594,753

## Abstract

Artificial intelligence (AI) systems that generate text and images, such as chatbots and image creation tools, have become increasingly powerful and widespread. However, these systems often produce unpredictable outputs that can be inappropriate, illogical, or harmful, especially in sensitive applications like healthcare, customer service, and education. For example, a medical chatbot might accidentally provide incorrect health information, or an image generator might create illogical content. This unpredictability prevents these technologies from being safely deployed in many important real-world applications where reliability is crucial. This project addresses this critical problem by developing new methods to give users precise control over what these artificial intelligence systems generate, while maintaining their creative capabilities. The research will enable safer deployment of generative artificial intelligence in sensitive settings and unlock new applications that require guaranteed reliability. The work will advance the field of artificial intelligence safety, support education through safer AI tools, and benefit society by enabling AI systems that can be confidently used in healthcare, education, and other critical domains.

This project develops novel approaches to control generative models through three interconnected research thrusts. First, the team will investigate language diffusion methods that integrate the controllability of diffusion models with autore

## Key facts

- **NSF award ID:** 2530143
- **Awardee organization:** Cornell University (NY)
- **SAM.gov UEI:** G56PUALJ3KT5
- **PI:** Kilian Q Weinberger
- **Primary program:** 01002526DB NSF RESEARCH & RELATED ACTIVIT
- **All programs:** ROBUST INTELLIGENCE, SMALL PROJECT
- **Estimated total:** $594,753
- **Funds obligated:** $594,753
- **Transaction type:** Standard Grant
- **Period:** 09/01/2025 → 08/31/2028

## Primary source

NSF Award Search: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2530143

## Citation

> US National Science Foundation, Award 2530143, RI: Small: Controlling Generative Models. Retrieved via AI Analytics 2026-06-08 from https://api.ai-analytics.org/grant/nsf/2530143. Licensed CC0.

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