arXiv:2605.24919v1 Announce Type: new Abstract: Hallucinations in Large Language Models (LLMs) represent a critical barrier to their reliable deployment, a vulnerability heavily exacerbated in non-English and resource-constrained contexts. Existing detection approaches that rely on output confidence heuristics or single-layer internal representations frequently fail to capture deep, complex factual inconsistencies across diverse languages. To address this, we introduce MultiHaluDet, a novel three-stage stacking framework that detects multilingual hallucinations by probing the full hidden state
As LLMs become more integrated into critical applications, the urgent need to address hallucinations, especially in multilingual and resource-constrained environments, becomes paramount for their reliable deployment.
This development can significantly enhance the trustworthiness and applicability of Large Language Models across diverse linguistic contexts, reducing risks associated with misinformation and improving international AI applications.
The ability to accurately detect hallucinations across multiple languages and within varied contexts marks a critical improvement in LLM reliability and fosters broader global adoption.
- · LLM developers
- · multilingual AI users
- · AI safety researchers
- · resource-constrained language communities
- · developers of unreliable LLM applications
- · companies relying on poor hallucination detection
Improved hallucination detection will lead to more robust and trustworthy LLM applications.
Enhanced reliability could accelerate the adoption of LLMs in sensitive sectors like finance, healthcare, and government across more languages.
The reduced risk of multilingual hallucinations might indirectly encourage greater investment in AI development for non-dominant languages and markets.
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Read at arXiv cs.CL