arXiv:2605.31363v1 Announce Type: new Abstract: Many languages are written in multiple scripts, requiring large language models (LLMs) to generate equivalent linguistic content in distinct orthographic forms. While prior work suggests that LLMs route information through shared latent representations, how they internally mediate script variation remains poorly understood. We study this question by first examining per-layer output distributions with the logit lens, which reveals consistent latent romanization during transliteration, and then through representational and mechanistic analyses of s
The paper leverages recent advancements in LLMs and interpretability tools (like logit lens) to probe their internal workings regarding multilingual script handling, a timely focus as LLMs become more globally pervasive.
Understanding how LLMs mediate script variation is crucial for developing more robust, equitable, and culturally sensitive AI, particularly as these models are deployed across diverse linguistic and orthographic landscapes.
This research provides deeper insight into LLM internal representations for multilingual tasks, potentially accelerating development of more efficient and accurate cross-script language processing as well as identifying potential biases or failure modes.
- · AI researchers
- · Multilingual AI developers
- · Global technology companies
- · Users of diverse scripts online
- · Companies with single-script AI solutions
- · Poorly generalized LLMs
Improved understanding of LLM internal mechanisms for script processing will lead to more effective multilingual AI models.
Enhanced cross-script capabilities could facilitate greater global digital inclusion and smoother international communication across different orthographies.
This could accelerate the adoption of AI in regions with complex linguistic diversity, driving new economic and social opportunities.
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Read at arXiv cs.CL