SOURCE-LINKED INTELLIGENCE

Controlling Large Language Models

CORDIS · observation · Publication date unknown

Controlling Large Language Models Large language models (LMs) are quickly becoming the backbone of many artificial intelligence (AI) systems, achieving state-of-the-art results in many tasks and application domains. Despite the rapid progress in the field, AI systems suffer from multiple flaws inherited from the underlying LMs: biased behavior, out-of-date information, confabulations, flawed reasoning, and more. If we wish to control these systems, we must first understand how they work, and develop mechanisms to intervene, update, and repair them. However, the black-box nature of LMs makes them largely inaccessible to such interventions. In this proposal, our overarching goal is to: *Develop a framework for elucidating the internal mechanisms in LMs and for controlling their behavior in an efficient, interpretable, and safe manner.* To achieve this goal, we will work through four objectives. Fi

Read original source ↗ Open in workspace

recordType

award

status

SIGNED

region

EU

value

1500000

unit

EUR