# Generalization of Fine-Tuned Uncertainty Communication and Metacognition in Large Language Models

arXiv: https://arxiv.org/abs/2510.05126 · HTML: https://mlsanity.com/paper/2510.05126/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 52 references that are in the map.
- First posted on arXiv 30 Sep 2025.

## Builds on (most cited first, 30 of 30)

- [Training Verifiers to Solve Math Word Problems](https://mlsanity.com/paper/2110.14168/) (2021)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [Training Compute-Optimal Large Language Models](https://mlsanity.com/paper/2203.15556/) (2022)
- [TruthfulQA: Measuring How Models Mimic Human Falsehoods](https://mlsanity.com/paper/2109.07958/) (2021)
- [TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension](https://mlsanity.com/paper/1705.03551/) (2017)
- [Language Models (Mostly) Know What They Know](https://mlsanity.com/paper/2207.05221/) (2022)
- [MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark](https://mlsanity.com/paper/2406.01574/) (2024)
- [Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference](https://mlsanity.com/paper/2403.04132/) (2024)
- [Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMs](https://mlsanity.com/paper/2306.13063/) (2023)
- [Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human Feedback](https://mlsanity.com/paper/2305.14975/) (2023)
- [Teaching Models to Express Their Uncertainty in Words](https://mlsanity.com/paper/2205.14334/) (2022)
- [A Comprehensive Overview of Large Language Models](https://mlsanity.com/paper/2307.06435/) (2023)
- [LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Models](https://mlsanity.com/paper/2308.11462/) (2023)
- [Hallucination is Inevitable: An Innate Limitation of Large Language Models](https://mlsanity.com/paper/2401.11817/) (2024)
- [A Survey of Confidence Estimation and Calibration in Large Language Models](https://mlsanity.com/paper/2311.08298/) (2023)
- [Why Language Models Hallucinate](https://mlsanity.com/paper/2509.04664/) (2025)
- [Large Legal Fictions: Profiling Legal Hallucinations in Large Language Models](https://mlsanity.com/paper/2401.01301/) (2024)
- [Calibrated Language Models Must Hallucinate](https://mlsanity.com/paper/2311.14648/) (2023)
- [Confidence Improves Self-Consistency in LLMs](https://mlsanity.com/paper/2502.06233/) (2025)
- [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)
- [SaySelf: Teaching LLMs to Express Confidence with Self-Reflective Rationales](https://mlsanity.com/paper/2405.20974/) (2024)
- [Large Language Models Must Be Taught to Know What They Don't Know](https://mlsanity.com/paper/2406.08391/) (2024)
- [Medical Hallucinations in Foundation Models and Their Impact on Healthcare](https://mlsanity.com/paper/2503.05777/) (2025)
- [Can Large Language Models Faithfully Express Their Intrinsic Uncertainty in Words?](https://mlsanity.com/paper/2405.16908/) (2024)
- [Calibrating Large Language Models with Sample Consistency](https://mlsanity.com/paper/2402.13904/) (2024)
- [Metacognition and Uncertainty Communication in Humans and Large Language Models](https://mlsanity.com/paper/2504.14045/) (2025)
- [Language Models Prefer What They Know: Relative Confidence Estimation via Confidence Preferences](https://mlsanity.com/paper/2502.01126/) (2025)
- [Fragile Preferences: A Deep Dive Into Order Effects in Large Language Models](https://mlsanity.com/paper/2506.14092/) (2025)

## Built on by (most cited first, 4 of 4)

- [How do LLMs Compute Verbal Confidence](https://mlsanity.com/paper/2603.17839/) (2026)
- [Quantitative Introspection in Language Models: Tracking Emotive States Across Conversation](https://mlsanity.com/paper/2603.18893/) (2026)
- [Quantisation Reshapes the Metacognitive Geometry of Language Models](https://mlsanity.com/paper/2604.08976/) (2026)
- [Metacognition in LLMs: Foundations, Progress, and Opportunities](https://mlsanity.com/paper/2607.11881/) (2026)

## Abstract (arXiv)

Background. Large language models are increasingly used in settings where confident but incorrect answers can mislead users. Reliable uncertainty communication requires a form of metacognition: monitoring when one's own answers are likely to be correct. Yet models' stated confidence is often poorly aligned with answer correctness. We test whether supervised fine-tuning improves uncertainty communication and whether gains transfer across domains and task formats. Methods. We fine-tuned two models on general knowledge, mathematics, and open-ended trivia questions. We evaluated single-question confidence estimation, in which the model reports numeric confidence for one answer, and pairwise confidence comparison, in which it chooses which of two questions it is more likely to answer correctly. We tested held-out questions from training domains and new medical, legal, and truthfulness benchmarks. We assessed calibration, discrimination, and answer accuracy before and after fine-tuning. Results. Here we show that fine-tuning improves alignment between stated confidence and observed accuracy and increases the model's ability to assign higher confidence to correct than to incorrect answers. Gains occur within training domains and, to a lesser extent, in new domains. However, single-task training does not reliably transfer between single-question confidence estimation and pairwise confidence comparison. Multitask fine-tuning produces broader gains in the models and tasks studied here. Conclusions. Uncertainty communication in large language models is trainable, but transfer across metacognitive tasks is limited. Joint training on multiple confidence tasks may support broader generalization, although further tests across model families and metacognitive tasks are needed.