Timothy Baldwin

Deferring to a larger LLM only when a smaller LLM is uncertain can match the performance of the larger model alone, while slashing inference costs.

LLMs can act as subject matter experts to conduct cost-effective, nuanced interviews, potentially revolutionizing early-stage hiring decisions.

LLMs are shockingly susceptible to generating fake news under jailbreak attacks, especially when it comes to English and U.S.-related topics, exposing a dangerous safety imbalance.

Distilling language models just got more efficient: a new loss function focuses on the long tail of token probabilities, boosting performance without extra compute.