This paper tested how well 33 frontier language models know when they're right or wrong (metacognition) across six different knowledge domains from the MMLU benchmark. The author gave models 1,500 questions, asked them to rate their confidence (0-100), and measured whether high confidence actually predicted correct answers. Every model showed big variation across domains — models were consistently good at monitoring their accuracy on applied/professional knowledge but struggled with formal reasoning and natural science.
Main takeaways:
- All models with above-chance metacognition showed significant domain-to-domain variation that aggregate scores hide
- Applied/Professional knowledge was easiest to monitor (mean AUROC = 0.742, top-2 in 21/33 models); Formal Reasoning and Natural Science were hardest (bottom-2 in 27/33 models)
- Some model families (Anthropic, Gemini, Qwen) show consistent within-family patterns in which domains are hard vs. easy, while others (DeepSeek, Gemma, OpenAI) don't
- Three models that failed on binary yes/no confidence probes worked fine when asked for 0-100 scores, showing that the prompt format matters a lot
- Gemma 4 31B showed a massive +0.202 AUROC improvement over Gemma 3 27B, suggesting newer models are getting better at knowing what they know