arXiv:2606.24579v1 Announce Type: new Abstract: Parametric knowledge in Large Language Models is not equally accessible across languages. As a result, standard inference techniques often struggle to surface localized facts, leading to failures in cross-lingual knowledge transfer and consistency. In this work, we investigate techniques for accessing hidden factual knowledge by exploring cross-lingual prompting strategies. We identify four inherent dimensions of cross-lingual exploration that directly govern parametric knowledge retrieval and evaluate them on multilingual factual benchmarks cove
Published in 2026, this research indicates a current focus on refining LLMs for global applications, essential for their commercial viability and ethical deployment.
This research addresses a critical limitation in large language models (LLMs) regarding factual consistency and knowledge transfer across languages, impacting their real-world utility and equitable access.
Techniques for cross-lingual interaction with LLMs improve, potentially leading to more reliable and globally consistent AI applications, and reducing linguistic bias in AI-driven information retrieval.
- · Multilingual AI developers
- · Global enterprises using LLMs
- · Users in non-English speaking markets
- · AI ethics and fairness advocates
- · Monolingual LLM development approaches
- · AI applications heavily reliant on English-centric data
Improved cross-lingual capabilities in LLMs enable more accurate and culturally nuanced AI services worldwide.
Enhanced cross-lingual parametric knowledge reduces information disparity and fosters more inclusive AI tools, potentially impacting geopolitical information flows.
Nations seeking to develop their own AI capabilities may benefit from more universal access to foundational LLM knowledge, reducing dependency on models trained primarily on dominant languages.
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Read at arXiv cs.CL