Papers by Mengdi Huai
Quantifying and Understanding Uncertainty in Large Reasoning Models (2026.acl-long)
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| Challenge: | Existing methods for estimating generation uncertainty do not provide finite-sample guarantees for reasoning-answer generation. |
| Approach: | They propose a method that provides the uncertainty of the reasoning-answer structure with statistical guarantees. |
| Outcome: | The proposed method disentangles reasoning quality from answer correctness while establishing theoretical guarantees for efficient explanation methods. |
Quantifying Uncertainty in Natural Language Explanations of Large Language Models for Question Answering (2025.findings-emnlp)
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| Challenge: | Large language models (LLMs) have shown strong capabilities, enabling concise, context-aware answers in question answering tasks. |
| Approach: | They propose a framework that provides valid uncertainty guarantees for LLMs . they also propose 'model-agnostic' uncertainty estimation method that maintains valid guarantees even under noise. |
| Outcome: | The proposed method provides valid uncertainty guarantees even under noise. |