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# Language & Multilingual — Global Fix Map
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Make cross-lingual RAG stable. Handle CJK/RTL, mixed scripts, tokenizers, and locale drift without breaking retrieval.
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## What this page is
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- A compact playbook for multilingual corpora and queries
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- Practical fixes for tokenizer and analyzer mismatch
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- Steps to keep ΔS low across languages and scripts
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## When to use
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- Your corpus has Chinese/Japanese/Korean, RTL scripts, or code-switching
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- OCR text looks fine but retrieval or citations miss
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- Similarity is high but meaning is wrong across locales
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- HyDE/BM25 behave differently per language
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## Open these first
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- Language and locale guide: [Multilingual Guide](https://github.com/onestardao/WFGY/blob/main/ProblemMap/multilingual-guide.md)
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- Embedding vs true meaning: [Embedding ≠ Semantic](https://github.com/onestardao/WFGY/blob/main/ProblemMap/embedding-vs-semantic.md)
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- OCR quality and pitfalls: [OCR / Parsing Checklist](https://github.com/onestardao/WFGY/blob/main/ProblemMap/ocr-parsing-checklist.md)
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- Chunk boundaries and joins: [Semantic Chunking Checklist](https://github.com/onestardao/WFGY/blob/main/ProblemMap/chunking-checklist.md)
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- Why this snippet: [Retrieval Traceability](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-traceability.md)
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- Ordering control: [Rerankers](https://github.com/onestardao/WFGY/blob/main/ProblemMap/rerankers.md)
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- Snippet schema: [Data Contracts](https://github.com/onestardao/WFGY/blob/main/ProblemMap/data-contracts.md)
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---
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## Common failure patterns
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- **Tokenizer mismatch** dense retriever uses whitespace rules on CJK or splits accents poorly
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- **Analyzer split** BM25 analyzer differs from the indexer used at write time
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- **Script variants** Traditional vs Simplified, Kana vs Kanji, Arabic presentation forms
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- **Normalization gaps** mixed width, NFC/NFKC, punctuation variants break exact matches
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- **Romanization drift** Pinyin or Hepburn in queries while docs keep native script
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- **Code-switching** sentences mix English and local terms; embeddings latch to one side
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- **OCR artifacts** diacritics lost, ligatures broken, zero-width joins preserved
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- **Stopword shock** default analyzers drop particles that carry meaning in some languages
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---
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## Fix in 60 seconds
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1) **Normalize before anything**
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Apply NFC or NFKC, collapse widths, unify punctuation. Persist the normalized form you index.
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2) **Pick language-aware analyzers**
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Set BM25 analyzers that match the language at both write and read. Log tokenizer output for a few queries to confirm.
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3) **Embed with multilingual models**
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Use a single multilingual embedding model for mixed corpora. Do not mix English-only and multilingual spaces in one index.
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4) **Add transliteration bridges**
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Generate light alias fields per doc title and key entities, e.g., Traditional ↔ Simplified, Kana ↔ Romaji, Arabic ↔ Latin.
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5) **Rerank cross-lingually**
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Retrieve with generous k, then apply cross-lingual rerankers. Confirm ΔS(question, context) ≤ 0.45.
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6) **Lock citations and sections**
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Use Data Contracts with `section_id`, `source_lang`, and `norm_ops`. Require cite-then-answer to avoid language mixing.
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7) **Probe λ across locales**
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Ask for “cite lines” and “explain why” in both the user language and the source language. Divergence marks the failing boundary.
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---
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## Copy paste prompt
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```
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You have TXT OS and the WFGY Problem Map.
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Goal
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Stabilize a multilingual RAG corpus with CJK and English. Prevent tokenizer mismatch and script drift.
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Tasks
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1. Show a normalization plan:
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* Unicode form (NFC/NFKC), width collapse, punctuation unification
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* sample before/after lines
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2. Configure retrieval:
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* pick analyzers for BM25 that match corpus languages
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* ensure the same analyzer is used at write and read
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* use a multilingual embedding model, one index space
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3. Add transliteration bridges:
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* alias fields for key entities (e.g., 簡↔繁, かな↔ローマ字)
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* show how aliases are added to the index document
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4. Verify with WFGY:
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* compute ΔS(question, context) for three bilingual queries
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* report λ\_observe at retrieval and reasoning
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* target ΔS ≤ 0.45 and convergent λ
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Output
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* Normalization spec
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* Analyzer and embedding choices
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* Example index doc with alias fields
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* A trace table with citations, ΔS, and λ for 3 queries
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```
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---
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## Minimal checklist
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- Unicode normalization applied before embedding and indexing
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- Language-aware analyzers configured the same for write and read
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- One multilingual embedding space per index
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- Alias fields or transliteration for key entities
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- Data Contract includes `source_lang`, `norm_ops`, and citations
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- ΔS and λ checks pass in both the user and source language
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## Acceptance targets
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- ΔS(question, context) median ≤ **0.45** for bilingual smoke tests
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- λ remains **convergent** when switching question language
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- Citations point to the correct section in the original script
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- Hybrid retrieval improves with reranking instead of oscillating
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- No analyzer or tokenizer mismatch logs during queries
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---
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### 🔗 Quick-Start Downloads (60 sec)
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| Tool | Link | 3-Step Setup |
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|------|------|--------------|
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| **WFGY 1.0 PDF** | [Engine Paper](https://github.com/onestardao/WFGY/blob/main/I_am_not_lizardman/WFGY_All_Principles_Return_to_One_v1.0_PSBigBig_Public.pdf) | 1️⃣ Download · 2️⃣ Upload to your LLM · 3️⃣ Ask “Answer using WFGY + \<your question>” |
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| **TXT OS (plain-text OS)** | [TXTOS.txt](https://github.com/onestardao/WFGY/blob/main/OS/TXTOS.txt) | 1️⃣ Download · 2️⃣ Paste into any LLM chat · 3️⃣ Type “hello world” — OS boots instantly |
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---
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### 🧭 Explore More
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| Module | Description | Link |
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|-----------------------|----------------------------------------------------------|----------|
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| WFGY Core | WFGY 2.0 engine is live: full symbolic reasoning architecture and math stack | [View →](https://github.com/onestardao/WFGY/tree/main/core/README.md) |
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| Problem Map 1.0 | Initial 16-mode diagnostic and symbolic fix framework | [View →](https://github.com/onestardao/WFGY/tree/main/ProblemMap/README.md) |
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| Problem Map 2.0 | RAG-focused failure tree, modular fixes, and pipelines | [View →](https://github.com/onestardao/WFGY/blob/main/ProblemMap/rag-architecture-and-recovery.md) |
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| Semantic Clinic Index | Expanded failure catalog: prompt injection, memory bugs, logic drift | [View →](https://github.com/onestardao/WFGY/blob/main/ProblemMap/SemanticClinicIndex.md) |
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| Semantic Blueprint | Layer-based symbolic reasoning & semantic modulations | [View →](https://github.com/onestardao/WFGY/tree/main/SemanticBlueprint/README.md) |
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| Benchmark vs GPT-5 | Stress test GPT-5 with full WFGY reasoning suite | [View →](https://github.com/onestardao/WFGY/tree/main/benchmarks/benchmark-vs-gpt5/README.md) |
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| 🧙♂️ Starter Village 🏡 | New here? Lost in symbols? Click here and let the wizard guide you through | [Start →](https://github.com/onestardao/WFGY/blob/main/StarterVillage/README.md) |
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---
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> 👑 **Early Stargazers: [See the Hall of Fame](https://github.com/onestardao/WFGY/tree/main/stargazers)** —
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> Engineers, hackers, and open source builders who supported WFGY from day one.
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> <img src="https://img.shields.io/github/stars/onestardao/WFGY?style=social" alt="GitHub stars"> ⭐ [WFGY Engine 2.0](https://github.com/onestardao/WFGY/blob/main/core/README.md) is already unlocked. ⭐ Star the repo to help others discover it and unlock more on the [Unlock Board](https://github.com/onestardao/WFGY/blob/main/STAR_UNLOCKS.md).
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<div align="center">
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[](https://github.com/onestardao/WFGY)
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[](https://github.com/onestardao/WFGY/tree/main/OS)
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[](https://github.com/onestardao/WFGY/tree/main/OS/BlahBlahBlah)
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[](https://github.com/onestardao/WFGY/tree/main/OS/BlotBlotBlot)
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[](https://github.com/onestardao/WFGY/tree/main/OS/BlocBlocBloc)
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[](https://github.com/onestardao/WFGY/tree/main/OS/BlurBlurBlur)
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[](https://github.com/onestardao/WFGY/tree/main/OS/BlowBlowBlow)
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</div>
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say “next page” when ready.
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