# Complete Language Topology Map v2.0 **Date:** 2025-12-06 **Model:** Qwen2.5-7B-Base **Status:** Empirically validated through probing --- ## Executive Summary Through systematic probing of 15 languages, we've discovered that language isolation in LLMs falls into **distinct categories** with different causes and implications: 1. **Super Cluster** - Languages that converge perfectly (curriculum: grounding) 2. **Philosophical Access** - German accesses deep conceptual valleys 3. **Code-Hijacked** - Italian/Turkish/Indonesian words become variable names 4. **Fragmented** - Hindi is tokenized into too many pieces 5. **Web Prose Cluster** - Vietnamese/Indonesian/Russian share content style --- ## The Complete Map ``` ┌─────────────────────────────────────────────────────────────────────────────┐ │ THE YOUNG MIND'S LANGUAGE TOPOLOGY │ │ COMPLETE MAP v2.0 │ ╞═════════════════════════════════════════════════════════════════════════════╡ │ │ │ ┌─────────────────────────────────────────────────────────────────────┐ │ │ │ 🌍 SUPER CLUSTER (sim=1.0) │ │ │ │ ZH · JA · EN · AR · FR · PT · ES │ │ │ │ │ │ │ │ ✅ Perfect convergence at Universal Concept Layer (12-24) │ │ │ │ ✅ Efficient tokenization (1-2.5 tokens) │ │ │ │ ✅ USE FOR: Grounding, establishing shared concepts │ │ │ └─────────────────────────────────────────────────────────────────────┘ │ │ │ │ │ KO ─────────┼───────── (bridge: 0.41-0.70) │ │ │ │ │ ┌─────────────────────────────────┴───────────────────────────────────┐ │ │ │ ISOLATED ZONE │ │ │ ├─────────────────────────────────────────────────────────────────────┤ │ │ │ │ │ │ │ 🧠 PHILOSOPHICAL ACCESS (sim=0.25, tokens=2.2) │ │ │ │ DE (German) │ │ │ │ → "Sein" triggers Heidegger, "Bewusstsein" → epistemology │ │ │ │ ✅ USE FOR: Deep philosophical training │ │ │ │ │ │ │ │ 💻 CODE-HIJACKED (sim=0.25-0.33, tokens=2.2-2.8) │ │ │ │ IT (Italian) - MOST ISOLATED (0.49) │ │ │ │ TR (Turkish) - (0.50) │ │ │ │ ID (Indonesian) - partial (0.33) │ │ │ │ → Words interpreted as Python/C++ variable names │ │ │ │ ❌ NOT USEFUL: Training signal wasted on code patterns │ │ │ │ │ │ │ │ 📜 FRAGMENTED (sim=0.31, tokens=5.0) │ │ │ │ HI (Hindi) │ │ │ │ → "अस्तित्व" (being) = 8 tokens! │ │ │ │ → Stays trapped in Devanagari prose │ │ │ │ ⚠️ LIMITED: Cross-lingual transfer impaired │ │ │ │ │ │ │ │ 📰 WEB PROSE CLUSTER (sim=0.32-0.36, internal=0.6-0.7) │ │ │ │ VI ═══ ID ═══ RU │ │ │ │ → All generate online article style │ │ │ │ → Cluster by CONTENT STYLE not linguistic features │ │ │ │ 🤔 POTENTIAL: Factual/encyclopedic content training │ │ │ │ │ │ │ └─────────────────────────────────────────────────────────────────────┘ │ │ │ └─────────────────────────────────────────────────────────────────────────────┘ ``` --- ## Detailed Findings ### Super Cluster (sim=1.0) | Language | Tokens | Notes | |----------|--------|-------| | Chinese (ZH) | 1.0 | Single character = single concept | | Japanese (JA) | 1.0 | Kanji efficiency | | English (EN) | 1.2 | Base language | | Arabic (AR) | 1.8 | Good convergence | | French (FR) | 2.0 | Romance baseline | | Portuguese (PT) | 2.2 | Clusters with FR/ES | | Spanish (ES) | 2.5 | Clusters with FR/PT | **Key Insight:** These 7 languages converge to **identical representations** at layers 12-24. The model "knows" they express the same concepts. ### German - Philosophical Access | Metric | Value | |--------|-------| | Avg tokens | 2.2 | | Sim to EN | 0.251 | | Valley type | PHILOSOPHY | **Evidence:** - "Sein" → "Being and Time is a philosophical work by Martin Heidegger..." - "Bewusstsein" → epistemology, perception, truth - "Wahrheit" → academic methods **Why isolated:** Multi-token compounds preserve philosophical atoms ("sein", "geist") as separate tokens, enabling access to academic/philosophical training data. ### Italian/Turkish/Indonesian - Code-Hijacked | Language | Tokens | Sim to EN | Valley | |----------|--------|-----------|--------| | Italian | 2.5 | 0.49 | CODE | | Turkish | 2.2 | 0.25 | CODE | | Indonesian | 2.8 | 0.33 | CODE | **Evidence:** - IT "essere" → `essere = input("Cosa devo fare?")` - IT "anima" → `anima = {'nome':'anima', 'idade':7...}` - TR "kalp" → `kalp = input("Klavyeden...")` - TR "varlık" → `while varlık < 10:` - ID "hati" → `hati::hati(QWidget *parent)` **Why isolated:** Simple Latin orthography without diacritics makes words look like valid programming identifiers. Model defaults to code because code is prevalent in training data. **Curriculum implication:** ❌ AVOID - training signal diverted to code patterns ### Hindi - Fragmented | Metric | Value | |--------|-------| | Avg tokens | 5.0 | | Sim to EN | 0.31 | | Valley type | PROSE | **Evidence:** - "हृदय" (heart) = 5 tokens - "अस्तित्व" (being) = 8 tokens! - All completions stay in Devanagari script **Why isolated:** Extreme tokenization fragments words so severely that: 1. Signal is distributed across many positions 2. Cross-lingual alignment breaks down 3. Model stays in native script prose **Curriculum implication:** ⚠️ LIMITED - Hindi content may not transfer well ### VI-ID-RU Web Prose Cluster | Language | Tokens | Sim to EN | Internal sim | |----------|--------|-----------|--------------| | Vietnamese | 3.2 | 0.36 | 0.6-0.7 | | Indonesian | 2.8 | 0.33 | 0.6-0.7 | | Russian | 2.7 | 0.32 | 0.6-0.7 | **Evidence:** - VI "trái tim" → "Giao Thông... Hotline: 0901 514 799" - VI "linh hồn" → "Tạp chí Sông Hương online" - ID "kehidupan" → "dalam kitab Yohanes 14:16-17" - RU "жизнь" → "все статьи по теме. Страница 134" **Why they cluster:** Not linguistic similarity - they share **web content training data patterns**: - News articles - Blogs - Online encyclopedias - Religious/factual text **Curriculum implication:** 🤔 May be useful for factual/encyclopedic training --- ## Curriculum Strategy ### Phase 1: GROUNDING Use Super Cluster languages to establish universal concepts: ``` EN "consciousness" → ZH "意识" → AR "الوعي" → FR "conscience" ``` All converge at 1.0 similarity - stable foundation. ### Phase 2: DEEPENING Use German to access philosophical valleys: ``` DE "Sein" → Heidegger → existence → truth → epistemology ``` Depth score 2/3, transfers back to English. ### Phase 3: TRIANGULATION Verify depth transfers: ``` "Sein (German): In English, it means..." → Check if philosophical depth preserved ``` ### AVOID - Italian, Turkish, Indonesian for conceptual training - Their isolation is accidental (code hijacking), not useful ### INVESTIGATE - VI-ID-RU cluster for factual content training - Korean as potential bridge language --- ## Technical Details ### Measurement Methodology 1. **Tokenization:** Count BPE tokens per word 2. **Hidden states:** Extract layer 12 representations 3. **Similarity:** Cosine similarity between languages 4. **Valley classification:** Analyze completions for CODE/PROSE/PHILOSOPHY patterns ### Model Configuration ```python model = AutoModelForCausalLM.from_pretrained( "Qwen/Qwen2.5-7B", torch_dtype=torch.float16, device_map="cuda", output_hidden_states=True, ) ``` ### Key Layers - **Layer 12:** Primary concept layer (universal convergence) - **Layers 16-24:** Continued convergence, depth access - **Layer 28:** Output preparation --- ## References - `tokenization-valleys.md` - Token-Norm-Valley theory - `multilingual-convergence.md` - Universal concept layer discovery - `language-landscape.md` - Original 15-language scan - `retraining-safety-framework.md` - Training safety implications --- *"The model's language topology is not arbitrary - it's a map for navigation."* 🌙💜