arXiv:2605.31220v1 Announce Type: cross Abstract: Confidence estimation (CE), i.e. quantifying the reliability of a model's prediction, has attracted great interest in the context of large language models (LLMs). However, most studies focus on English, ignoring the multilingual reality of LLM usage, while many CE methods degrade or require retraining across languages. To address this gap, we investigate whether multilingual LLMs encode shared, language-transferable confidence features. We use a lightweight linear probe that predicts answer correctness directly from intermediate representations
The proliferation of LLMs across diverse linguistic contexts necessitates robust confidence estimation, driving research into multilingual solutions beyond English-centric approaches.
Reliable cross-lingual confidence estimation is critical for deploying LLMs in global, high-stakes applications, influencing trust and mitigating risks associated with misinterpretations in non-English languages.
This research suggests that multilingual LLMs can inherently encode language-transferable confidence features, potentially simplifying the development of robust, globally applicable AI systems without extensive model retraining per language.
- · Multilingual AI developers
- · Global enterprises deploying LLMs
- · Users of non-English LLMs
- · Monolingual AI research
- · Companies relying on language-specific CE models
Improved reliability and safety of LLMs in diverse linguistic environments.
Accelerated adoption of LLMs in non-English markets and critical international applications.
Reduced costs and increased efficiency in developing and maintaining global AI products, potentially leveling the playing field for non-English speaking AI innovation.
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Read at arXiv cs.LG