Summary

Large Language Models (LLMs) build internal representations of the world by statistically learning from vast corpora. Since language is a symbolic approximation of reality, the resolution of an LLM’s virtual world model may depend heavily on the expressive granularity of its training language.

This post argues that Japanese, due to its multi-layered script system, high-context structure, and culturally embedded subtleties, allows LLMs to construct a finer-grained internal model of the world than English. 

1. Language as a Model of the World

Language is not just a communication medium—it discretizes the world into labeled, relational components. When an LLM learns from language, it builds a probabilistic model of the world’s structure—much like assembling a city from blocks.

Smaller blocks = more detail. Coarser blocks = more approximation. Thus, language granularity affects the resolution of this virtual world.

2. Japanese as a High-Granularity Language

Japanese exhibits extraordinary linguistic richness:

• Three scripts (hiragana, katakana, kanji)
• Flexible, context-dependent grammar
• Frequent ellipsis (omitted subjects/objects)
• Politeness levels and indirectness encoding social nuance

This results in multi-dimensional encoding and dense context. A single Japanese sentence can contain emotional and contextual subtleties that are harder to capture in English.

3. How Language Shapes Virtual World Resolution

An LLM trained in English builds a model using “English blocks”—logical, linear, and standardized.

A Japanese-trained LLM, however, constructs its world from “Japanese blocks”—subtle, emotionally rich, and fluidly structured. This might enable higher-resolution internal world models.

4. Cultural Evidence: Subtlety in Japanese Expression

Japan’s creative exports—manga, anime, games—are globally recognized for their emotional subtlety and symbolic nuance. These media rely heavily on silence, pacing, and implication.

Culturally, the Japanese practice of “reading the air” (空気を読む) reflects the ability to sense unspoken intent or mood—a skill analogous to how LLMs infer meaning from implicit statistical patterns, but perhaps with higher emotional and perceptual resolution.

5. Case Study: Phishing Scam Spike in 2025

In 2025, Japan saw an unprecedented spike in phishing scams written in Japanese. Why is this noteworthy?

Because it marked the first time LLM-generated Japanese reached a level of fluency that could deceive native speakers. Prior attempts felt robotic or awkward. But in 2025, attackers using LLMs produced Japanese that sounded entirely natural.

This isn’t just a milestone for LLMs—it highlights that Japanese was previously too complex to convincingly simulate. That it now can be mimicked suggests LLMs have only just surmounted a significant linguistic and cognitive barrier.

Conclusion

The language used to train an AI doesn’t just affect how it speaks—it shapes how it models, feels, and thinks about the world.

If so, Japanese may not merely be another dataset.
It could be a key to unlocking deeper AI cognition.