Adapt to Japanese: vocabulary, notation and tasks

Original conceptual diagram. Arrows show the dependency or decision sequence, not measured performance.

1. 1Original Japanese text
2. 2reversible normalization
3. 3tokenizer and retrieval checks
4. 4meaning-preserving output
5. 5task scoring

Before you begin

Prerequisite: Choose continued pretraining and preference optimization. Estimated study time: 50 minutes.

This chapter is an editorial guide. The exercise is UNRUN (not-run); no model-specific token counts or quality improvements have been measured here.

What you will learn

• Investigate how tokenizers and notation variations affect efficiency and quality.
• Distinguish adaptation for Japanese tasks from language-teaching applications.

What does adapting to Japanese mean?

Here, Japanese adaptation means enabling a model to handle Japanese services and business data appropriately. It does not refer only to teaching the language to Japanese learners. Choose problems specific to the intended service: mixtures of kanji, kana and Latin letters or digits; abbreviations; polite and plain styles; OCR of vertical text; or variations in written forms.

A multilingual model card that lists Japanese support does not guarantee accuracy in a particular domain. A model with natural general conversation may still struggle to distinguish product model numbers, contract conditions or specialist abbreviations. Start by creating a short evaluation that resembles the actual inputs.

The Qwen example model card lists Japanese among its supported languages. That is a provider description of the model family; the notation, specialist terminology and task checks below remain unrun local evaluations.

Observe tokenization and normalization

When Japanese expressions are split into many small tokens, the same content may consume more context and processing. Compare token counts using the candidate models, and measure whether meaning is preserved as well as how quickly the task runs. Adding to or changing a tokenizer is not a lightweight configuration adjustment: it can require embedding training and introduce compatibility issues.

Normalizing full-width and half-width characters or written forms can help retrieval. Careless normalization, however, can lose the meaning of product numbers, units and symbols. Preserve both the original and normalized text so the source can be traced. Use synonym dictionaries to expand terminology candidates, not to declare different concepts equivalent without justification.

Make the Japanese evaluation concrete

Include variations between kanji and kana, abbreviations, negation, double negation, omitted subjects, numbers and units, and proper nouns in the evaluation examples. Do not score only the naturalness of polite language. Check whether the original conditions are preserved and whether the model can ask for clarification instead of guessing an unknown term.

If the goal is simply to look up specialist terminology, first try dictionary RAG with sources. SFT is a candidate when output wording or structure needs to be consistent. Consider adaptation to a large domain corpus as a separate, later stage. Do not assume collecting more data always improves results: check rights, duplicates, provenance and transcription errors first.

Roles and input → process → output

Role	Responsibility
Learner	Design the hypothesis, data and evaluation.
Model	Attempt the specified transformation.
Application	Enforce limits, validation and permissions.

Stage	What it contains
Input	Public or self-authored data and glossaries that imitate a real Japanese use case.
Process	Observe tokenization and normalization → choose a method → evaluate.
Output	Improvement results separated by notation, meaning and format.

Workflow

1. List the Japanese language features of the intended task.
2. Design normalization that preserves the original text.
3. Compare token counts for candidate models.
4. Try a dictionary or RAG and prompts first.
5. Limit additional training to a specific improvement target.

Exercise: separate variation from different meaning

Status: UNRUN (not-run). Write five pairs with the same meaning but different written forms, and five pairs that look similar but have different meanings.

Deliverable: an evaluation of all ten pairs, with retrieval and answers assessed separately.

Completion check: score the preservation of meaning, not just visual matches.

Quality checklist

• Have you investigated how the tokenizer and notation variations affect efficiency and quality?
• Is adaptation for Japanese tasks distinct from a language-teaching application in your plan?
• Does your evaluation check meaning as well as surface similarity?

Pitfalls and failure diagnosis

A Japanese-support label is not a certificate of competence in a specialist domain. Adding vocabulary does not mean the model immediately understands it.

Caveat	What to check
Treating declared Japanese support as proof of domain competence.	Evaluate the domain’s abbreviations, notation and task requirements explicitly.
Assuming a vocabulary addition immediately creates understanding.	Compare outputs on the fixed task before and after adaptation, including mistaken term use.

Use the experiment worksheet to plan or record this experiment.