Rachel Freedman

Abstract AI safety researchers often rely on LLM “judges” to qualitatively evaluate the output of separate LLMs. We try this for our own interpretability research, but find that our LLM judges are often deeply biased. For example, we use Llama2 to judge whether movie reviews are more “(A) positive” or...

Tl;dr: An optimal CIRL agent is corrigible under a certain problem formulation and given certain assumptions. By examining three key assumptions—that the game is played only once, that the human is rational, and that human feedback is free—we demonstrate how minor changes can lead to incorrigible CIRL agents. This suggests...