Pierre-Alexandre Mattei

Linear and geometric pooling aren't just common ensemble methods, they're theoretically optimal for consistent gains, while other aggregation rules can actively hurt performance.

Temperature scaling, the go-to method for uncertainty control, is formally proven to increase classifier uncertainty, but its impact on LLM diversity is questioned, revealing nuances in its application.