arXiv:2605.26293v1 Announce Type: new Abstract: Prior work establishes that controlled contrastiveness between self-generated responses from large language models, set via reward scores, improves downstream preference tuning in English. We extend this method to multiple languages and evaluate two models across a total of 14 high and low-resource languages on a diverse set of tasks. Our central finding is that cross-lingual contrastive preference tuning on self-generations (CroCo) transfers without language-specific preference annotation. A reward model trained on English preferences (atop a mu
This research arrives as the AI industry is aggressively pursuing ways to make large language models more efficient and globally applicable without extensive, language-specific data labeling efforts.
The ability to transfer preference tuning across diverse languages using English preferences suggests a significant leap in developing multilingual AI, reducing development costs and time for non-English applications.
AI model development for non-English languages can become significantly more accessible and cost-effective, potentially broadening the global applicability and impact of advanced AI without requiring localized human annotation.
- · Multilingual AI developers
- · Non-English speaking markets
- · Organizations with global user bases
- · Companies reliant on language-specific data labeling services
- · Monolingual AI development approaches
Cross-lingual AI models will become more performant and widely available.
This could accelerate AI adoption in emerging markets with diverse linguistic landscapes.
Reduced barriers to entry for multilingual AI may foster new competitive dynamics and innovation hubs outside traditional English-speaking tech centers.
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Read at arXiv cs.CL