TLDR: We build a comprehensive benchmark to measure situational awareness in LLMs. It consists of 16 tasks, which we group into 7 categories and 3 aspects of situational awareness (self-knowledge, situational inferences, and taking actions).

We test 19 LLMs and find that all perform above chance, including the pretrained GPT-4-base (which was not subject to RLHF finetuning). However, the benchmark is still far from saturated, with the top-scoring model (Claude-3.5-Sonnet) scoring 54%, compared to a random chance of 27.4% and an estimated upper baseline of 90.7%.

This post has excerpts from our paper, as well as some results on new models that are not in the paper.

Links: Twitter thread, Website (latest results + code), Paper

The structure of our benchmark. We define situational awareness and break it down into three aspects.  We test these aspects across 7 categories of task.  Note: Some questions have been slightly simplified for [...]