Authors: Shashwat Goel, Joschka Struber, Ilze Amanda Auzina, Karuna K Chandra, Ponnurangam Kumaraguru, Douwe Kiela, Ameya Prabhu, Matthias Bethge, Jonas Geiping
Abstract: As Language Model (LM) capabilities advance, evaluating and supervising them
at scale is getting harder for humans. There is hope that other language models
can automate both these tasks, which we refer to as “AI Oversight”. We study
how model similarity affects both aspects of AI oversight by proposing a
probabilistic metric for LM similarity based on overlap in model mistakes.
Using this metric, we first show that LLM-as-a-judge scores favor models
similar to the judge, generalizing recent self-preference results. Then, we
study training on LM annotations, and find complementary knowledge between the
weak supervisor and strong student model plays a crucial role in gains from
“weak-to-strong generalization”. As model capabilities increase, it becomes
harder to find their mistakes, and we might defer more to AI oversight.
However, we observe a concerning trend — model mistakes are becoming more
similar with increasing capabilities, pointing to risks from correlated
failures. Our work underscores the importance of reporting and correcting for
model similarity, especially in the emerging paradigm of AI oversight.
Source: http://arxiv.org/abs/2502.04313v1
