Estimating Uncertainty from Reasoning: A Large-Scale Study of Multi- and Crosslingual MCQA Performance in LLMs
arXiv:2607.06327v1 Announce Type: cross Abstract: 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 r
The proliferation of LLMs globally necessitates robust uncertainty estimation across diverse languages, making this research timely as LLM capabilities expand beyond English. The research addresses a critical gap identified by the increasing deployment of LLMs in varied linguistic contexts.
Improving uncertainty estimation in multi- and cross-lingual LLMs is crucial for their reliable and safe deployment in global applications, reducing failures and increasing user trust. This directly impacts the commercial viability and ethical adoption of AI systems worldwide.
Current understanding of LLM uncertainty estimation shifts from an English-centric view to a multilingual perspective, highlighting performance differences across language resource settings and model architectures. This will guide future development of more globally robust and reliable LLMs.
- · Multilingual AI developers
- · Global enterprises deploying LLMs
- · Users of non-English languages
- · AI safety researchers
- · LLMs with poor multilingual uncertainty estimation
- · English-only focused AI development
- · Applications requiring high-stakes cross-lingual reliability
More robust and reliable LLM applications will emerge in diverse linguistic markets, particularly in high-stakes domains.
This will accelerate the adoption of advanced AI systems in non-English speaking regions, potentially fostering new economic growth centers.
The increased reliability of multilingual LLMs could contribute to greater global information democratization and reduce language barriers in access to AI-powered services.
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Read at arXiv cs.AI