Fine-Tuning Enhances Latent Metacognitive Capability in Language Models

Large language models are increasingly asked not only to answer questions, but also to judge whether they know enough to answer. We test a latent metacognitive capability hypothesis: models already contain internal structure supporting weak self-evaluation of answer uncertainty, and later training routes this structure into confidence reports and delegation decisions. This predicts that self-evaluation should track direct-answer uncertainty before task-specific fine-tuning, transfer between report and action formats, become more linearly recoverable from confidence-report states after training, and be causally affected by interventions on relevant directions. We test these predictions in Llama-3.1-8B using an Explicit Confidence Task (ECT), where stated confidence is compared to answer-option uncertainty from a separate direct-answer pass, and a Delegate Game (DG), where the model decides whether to answer or defer. The pre-trained model already shows above-chance alignment between stated confidence and direct-answer uncertainty, which improves after instruction tuning and LoRA fine-tuning. DG fine-tuning transfers to ECT despite using only binary answer/delegate labels. Mechanistically, confidence-report states increasingly contain direct-answer uncertainty information, confidence-report directions align with answer-certainty directions, and causal interventions affect confidence reports while largely preserving answer accuracy. These results support a limited form of latent metacognition: training routes internal uncertainty signals into self-evaluative reports and decisions.