Robustness, unlearning and control

In this episode of The Turing Talks, we dive into the latest research on removing hazardous knowledge from large language models (LLMs). We explore "The WMDP Benchmark," a new tool designed to assess LLMs' expertise in critical areas like biosecurity, cybersecurity, and chemical security. The episode also introduces RMU, a novel unlearning technique that strips harmful information without affecting overall model capabilities. We then turn to "Deep Forgetting & Unlearning," which tackles the challenge of eliminating unwanted knowledge embedded in LLMs’ internal workings. Join us for insights into safer, more responsibly scoped AI.