Introducing the Conceptual Reasoning Index
Associated announcement tweet. We are planning to release blog posts properly arguing the case for this kind of work in the future. tl;dr A core hope for managing AI risks is that AIs will help us understand the situation, plan for what lies ahead, and develop mitigations. Many tasks AIs would have to do for this purpose lack practical empirical feedback loops and require models to engage in the kinds of argumentation used in philosophy, AI futurism, and similar domains. To evaluate these capabilities, we develop a suite of three conceptual reasoning benchmarks. You can request access to our primary conceptual dataset, LMCA, through this form . We aggregate the benchmarks into the Conceptual Reasoning Index (CRI), available at conceptualreasoning.ai , where you can also find more details on our methodology. We will keep the website up to date as both new models and benchmarks are released. This work was done in collaboration with Anthropic. Background Once models can perform work that reduces AI risk at the level of human experts, AI(-assisted) output in the area might dwarf unassisted human output. This suggests that a major determinant of whether we address AI risks in time is how early we can automate or uplift this work, relative to high-risk capabilities. One way to influence this might be to selectively improve models' relevant skills, such as reasoning about how to govern and align AI and how to avoid catastrophic cooperation failures involving AI. Current AI training…
