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Source note3 min read

Estimating Uncertainty from Reasoning: A Large-Scale Study of Multi- and Crosslingual MCQA Performance in LLMs

Source

arXiv

arxiv.org/abs/2607.06327v1

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Plain-Language Brief

The researchers evaluate the performance of uncertainty estimation methods across 22 languages in high-, mid-, and low-resource settings using two human-curated question and answer datasets. They compare open and closed box UE methods, avoid LLM-as-a-judge and embedding-based scoring to minimize evaluation noise, and report three main findings: 1) promoting models to reason in English while keeping questions in low-resource languages improves UE performance, suggesting comprehension of low-resource languages is intact, with the reliability bottleneck being generation. 2) Prompting models to reason in English closes the UE performance gap between low and high-resource languages, indicating that the generation language matters more than the question language. 3) The choice of UE method should depend on model scale: at smaller scales, open-box probability-based methods outperform alternatives; at larger scales, closed-box self-verbalized uncertainty becomes superior. They also provide an analysis of threshold selection for selective prediction, offering guidance on calibrating abstention in multilingual settings.

What the Document Says

  • The research focuses on uncertainty estimation (UE) methods across 22 languages and different model sizes and architectures.
  • They use two human-curated Q&A datasets for evaluation, avoiding LLM-as-a-judge and embedding-based scoring to minimize evaluation noise.
  • They compare open and closed box UE methods.
  • Findings: 1) promoting models to reason in English while keeping questions in low-resource languages improves UE performance.
    1. Prompting models to reason in English closes the UE performance gap between low and high-resource languages.
    1. The choice of UE method should depend on model scale: at smaller scales, open-box probability-based methods outperform alternatives; at larger scales, closed-box self-verbalized uncertainty becomes superior.
  • They provide an analysis of threshold selection for selective prediction, offering guidance on calibrating abstention in multilingual settings.

Key Concepts Mentioned

  • Uncertainty estimation
  • LLMs
  • Q&A datasets
  • open box UE methods
  • closed box UE methods
  • prompting models
  • English
  • low-resource languages
  • model scales
  • self-verbalized uncertainty

Claims Made by the Document

  • UE enables LLM-powered systems to recognize when to abstain.
  • Existing research has predominantly focused on English.
  • Comprehension of low-resource languages is largely intact, with the reliability bottleneck being generation rather than understanding.
  • Generation language matters more than the question language.
  • At smaller scales, open-box probability-based methods outperform alternatives; at larger scales, closed-box self-verbalized uncertainty becomes superior.

Evidence Used

  • The study evaluates UE methods across multiple languages and model sizes.
  • They use human-curated Q&A datasets for evaluation.
  • They compare different UE methods and report findings on their performance.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

Uncertainty estimation (UE) enables LLM-powered systems to recognize when to abstain, yet existing research has predominantly focused on English. We present the first large-scale evaluation of UE methods across 22 languages, spanning high-, mid-, and low-resource settings. Using two human-curated Q&A datasets, we compare open and closed box UE methods (nine in total) across different model sizes and architectures while eliciting long-form reasoning, avoiding LLM-as-a-judge and embedding-based scoring, which can introduce evaluation noise. We report three main actionable findings. First, we find that prompting models to reason in English while keeping questions in low-resource languages substantially improves UE performance, suggesting that comprehension of low-resource languages is largely intact, and that the reliability bottleneck lies in generation rather than understanding. Second, prompting models to reason in English closes the UE performance gap between low and high-resource languages, demonstrating that generation language matters more than the question language. Third, the choice of UE method should depend on model scale: at smaller scales, open-box probability-based methods outperform alternatives; at larger scales, closed-box self-verbalized uncertainty becomes superior. Finally, we provide an analysis of threshold selection for selective prediction, offering guidance on calibrating abstention in multilingual settings.