# Uncertainty Estimation in Autoregressive Structured Prediction

arXiv: https://arxiv.org/abs/2002.07650 · HTML: https://mlsanity.com/paper/2002.07650/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Feb 2020.

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

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- [SAUP: Situation Awareness Uncertainty Propagation on LLM Agent](https://mlsanity.com/paper/2412.01033/) (2024)
- [Joint Evaluation of Answer and Reasoning Consistency for Hallucination Detection in Large Reasoning Models](https://mlsanity.com/paper/2506.04832/) (2025)
- [MMBoundary: Advancing MLLM Knowledge Boundary Awareness through Reasoning Step Confidence Calibration](https://mlsanity.com/paper/2505.23224/) (2025)
- [Provable Uncertainty Decomposition via Higher-Order Calibration](https://mlsanity.com/paper/2412.18808/) (2024)
- [Verbosity neq Veracity: Demystify Verbosity Compensation Behavior of Large Language Models](https://mlsanity.com/paper/2411.07858/) (2024)
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- [Beyond Scalars: Evaluating and Understanding LLM Reasoning via Geometric Progress and Stability](https://mlsanity.com/paper/2603.10384/) (2026)
- [Can We Trust the Performance Evaluation of Uncertainty Estimation Methods in Text Summarization?](https://mlsanity.com/paper/2406.17274/) (2024)
- [Variability Need Not Imply Error: The Case of Adequate but Semantically Distinct Responses](https://mlsanity.com/paper/2412.15683/) (2024)
- [Decoding Memories: An Efficient Pipeline for Self-Consistency Hallucination Detection](https://mlsanity.com/paper/2508.21228/) (2025)
- [Aligning Probabilistic Beliefs under Informative Missingness: LLM Steerability in Clinical Reasoning](https://mlsanity.com/paper/2512.00479/) (2025)
- [KinyaEmbed: Contrastive Sentence Embeddings for Kinyarwanda via Multi-Stage Curriculum Training](https://mlsanity.com/paper/2608.26941/) (2026)
- [From Answers to Interpretations: Rethinking Ambiguity-Induced Aleatoric Uncertainty Estimation in LLMs](https://mlsanity.com/paper/2609.04543/) (2026)
- [Also Small Models Can Reasonably Self-Evaluate Their Confidence](https://mlsanity.com/paper/2609.39478/) (2026)
- [Sequential Probabilistic Uncertainty Estimation for Parallel Multi-Agent Reasoning Systems](https://mlsanity.com/paper/2610.08901/) (2026)
- [From Uncertainty to Action: Learning to Steer LLM Agents](https://mlsanity.com/paper/2610.09115/) (2026)

## Abstract (arXiv)

Uncertainty estimation is important for ensuring safety and robustness of AI systems. While most research in the area has focused on un-structured prediction tasks, limited work has investigated general uncertainty estimation approaches for structured prediction. Thus, this work aims to investigate uncertainty estimation for autoregressive structured prediction tasks within a single unified and interpretable probabilistic ensemble-based framework. We consider: uncertainty estimation for sequence data at the token-level and complete sequence-level; interpretations for, and applications of, various measures of uncertainty; and discuss both the theoretical and practical challenges associated with obtaining them. This work also provides baselines for token-level and sequence-level error detection, and sequence-level out-of-domain input detection on the WMT'14 English-French and WMT'17 English-German translation and LibriSpeech speech recognition datasets.