# Metacognition and Uncertainty Communication in Humans and Large Language Models

arXiv: https://arxiv.org/abs/2504.14045 · HTML: https://mlsanity.com/paper/2504.14045/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 49 references that are in the map.
- First posted on arXiv 18 Apr 2025.

## Builds on (most cited first, 12 of 12)

- [Language Models (Mostly) Know What They Know](https://mlsanity.com/paper/2207.05221/) (2022)
- [Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMs](https://mlsanity.com/paper/2306.13063/) (2023)
- [Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Making](https://mlsanity.com/paper/2001.02114/) (2020)
- [Do Large Language Models Know What They Don't Know?](https://mlsanity.com/paper/2305.18153/) (2023)
- [Looking Inward: Language Models Can Learn About Themselves by Introspection](https://mlsanity.com/paper/2410.13787/) (2024)
- [R-Tuning: Instructing Large Language Models to Say `I Don't Know'](https://mlsanity.com/paper/2311.09677/) (2023)
- [What Large Language Models Know and What People Think They Know](https://mlsanity.com/paper/2401.13835/) (2024)
- [Relying on the Unreliable: The Impact of Language Models' Reluctance to Express Uncertainty](https://mlsanity.com/paper/2401.06730/) (2024)
- [Uncertainty Quantification and Confidence Calibration in Large Language Models: A Survey](https://mlsanity.com/paper/2503.15850/) (2025)
- [Tell me about yourself: LLMs are aware of their learned behaviors](https://mlsanity.com/paper/2501.11120/) (2025)
- [Metacognitive Capabilities of LLMs: An Exploration in Mathematical Problem Solving](https://mlsanity.com/paper/2405.12205/) (2024)
- [Perceptions of Linguistic Uncertainty by Language Models and Humans](https://mlsanity.com/paper/2407.15814/) (2024)

## Built on by (most cited first, 23 of 23)

- [How do LLMs Compute Verbal Confidence](https://mlsanity.com/paper/2603.17839/) (2026)
- [Self-Interpretability: LLMs Can Describe Complex Internal Processes that Drive Their Decisions](https://mlsanity.com/paper/2505.17120/) (2025)
- [Causal Evidence that Language Models use Confidence to Drive Behavior](https://mlsanity.com/paper/2603.22161/) (2026)
- [Understanding the Thinking Process of Reasoning Models: A Perspective from Schoenfeld's Episode Theory](https://mlsanity.com/paper/2509.14662/) (2025)
- [LLMs as Signal Detectors: Sensitivity, Bias, and the Temperature-Criterion Analogy](https://mlsanity.com/paper/2603.14893/) (2026)
- [Do LLMs Know What They Know? Measuring Metacognitive Efficiency with Signal Detection Theory](https://mlsanity.com/paper/2603.25112/) (2026)
- [TRIAGE: Evaluating Prospective Metacognitive Control in LLMs under Resource Constraints](https://mlsanity.com/paper/2605.13414/) (2026)
- [Generalization of Fine-Tuned Uncertainty Communication and Metacognition in Large Language Models](https://mlsanity.com/paper/2510.05126/) (2025)
- [Before you <think>, monitor: Implementing Flavell's metacognitive framework in LLMs](https://mlsanity.com/paper/2510.16374/) (2025)
- [Agents Require Metacognitive and Strategic Reasoning to Succeed in the Coming Labor Markets](https://mlsanity.com/paper/2505.20120/) (2025)
- [Metacognitive Sensitivity for Test-Time Dynamic Model Selection](https://mlsanity.com/paper/2512.10451/) (2025)
- [Fine-Tuning Language Models to Know What They Know](https://mlsanity.com/paper/2602.02605/) (2026)
- [Measuring (some aspects of) the metacognition of AI](https://mlsanity.com/paper/2603.29693/) (2026)
- [Verbal Confidence Saturation in 3-9B Open-Weight Instruction-Tuned LLMs: A Pre-Registered Psychometric Validity Screen](https://mlsanity.com/paper/2604.22215/) (2026)
- [Can LLMs Use Linguistic Uncertainty Markers to Reliably Reflect Intrinsic Confidence?](https://mlsanity.com/paper/2605.28778/) (2026)
- [Self-Reference in Large Language Models: The Introspection Threshold for Recursive Self-Improvement](https://mlsanity.com/paper/2607.04277/) (2026)
- [Machine Psychometrics: A Mathematical Psychology of Artificial Intelligence](https://mlsanity.com/paper/2605.23952/) (2026)
- [Metacognition in LLMs: Foundations, Progress, and Opportunities](https://mlsanity.com/paper/2607.11881/) (2026)
- [The Computational Basis of Confidence in Large Language Models](https://mlsanity.com/paper/2607.12447/) (2026)
- [Automatic Minds: Cognitive Parallels Between Hypnotic States and Large Language Model Processing](https://mlsanity.com/paper/2511.01363/) (2025)
- [Domain-level metacognitive monitoring in frontier LLMs: A 33-model atlas](https://mlsanity.com/paper/2605.06673/) (2026)
- [metasignal: A Python Package for Comprehensive Metacognitive Analysis and Decision-Making](https://mlsanity.com/paper/2607.29093/) (2026)
- [Evaluating Persistent Calibration under Evolving Model Knowledge](https://mlsanity.com/paper/2609.38797/) (2026)

## Abstract (arXiv)

Metacognition--the capacity to monitor and evaluate one's own knowledge and performance--is foundational to human decision-making, learning, and communication. As large language models (LLMs) become increasingly embedded in both high-stakes and widespread low-stakes contexts, it is important to assess whether, how, and to what extent they exhibit metacognitive abilities. Here, we provide an overview of current knowledge of LLMs' metacognitive capacities, how they might be studied, and how they relate to our knowledge of metacognition in humans. We show that while humans and LLMs can sometimes appear quite aligned in their metacognitive capacities and behaviors, it is clear many differences remain; attending to these differences is important for enhancing human-AI collaboration. Finally, we discuss how endowing future LLMs with more sensitive and more calibrated metacognition may also help them develop new capacities such as more efficient learning, self-direction, and curiosity.