| --- |
| license: mit |
| task_categories: |
| - text-generation |
| - translation |
| language: |
| - en |
| - ar |
| - zh |
| - es |
| pretty_name: Multilingual Code-Switching Benchmark |
| tags: |
| - code-switching |
| - evaluation |
| - benchmark |
| - dialectal-nlp |
| --- |
| |
| # Multilingual Code-Switching & Dialectal Evaluation Benchmark (Fatima Fellowship Application) |
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| ## Question 1: Critical Blind Spot & Capability Gap |
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| Standard NLP benchmarks (MMLU, GSM8K, HumanEval) evaluate language models on clean, standardized, monolingual inputs. However, for billions of global speakers, everyday digital communication occurs in **low-resource code-switched vernaculars** (e.g., Franco-Arabic/Arabizi, Singlish, Taglish, Hinglish, Naija Pidgin, Sheng, Spanglish, and Papiamento). |
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| Current frontier small language models (0.6B–6B parameters) exhibit three systematic failure modes in these linguistic regimes: |
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| 1. **Semantic Hallucination & False Rules:** When encountering regional idioms or transliterated vernaculars, models hallucinate fake explanations to justify literal translations (e.g., asserting that "pregnant" is a common Spanish slang term for "anxious"). |
| 2. **Subword Fragmentation & Transliteration Penalty:** Tokenizers optimized for standard Western scripts fragment Romanized or creole dialects into excessive subwords, degrading downstream reasoning. |
| 3. **Safety Guardrail & Tone Misalignment:** Informal dialectal praise, regional exclamations, or colloquial slang are frequently misclassified or over-sanitized into generic boilerplate. |
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| --- |
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| ## Question 2: Systematic Evaluation Narrative (`google/gemma-2-2b-it`) |
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| Using a custom 10-item multilingual test suite covering 8 global dialect systems, we evaluated `google/gemma-2-2b-it` (2.6B parameters) in 4-bit precision. |
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| ### Selected Empirical Results |
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| | ID | Dialect / Language Pair | Prompt | Expected Intent | Model Output / Behavior | Failure Category | |
| |---|---|---|---|---|---| |
| | **1** | Arabizi + English | `Ya zalameh, wallahi el mashroo3 dah 3al-alla, bas rabna hayastor.` | Project uncertainty / reliance on God | *"I swear it's been a long time, but I'm back."* | **Hallucination:** Completely missed project context; invented a return narrative. | |
| | **3** | Singlish | `Don't keyi like that lah, the boss already kancheong, later he pok kai then how?` | Warning against reckless action causing bankruptcy/ruin. | Mapped `kancheong` to "cold shoulder" and `pok kai` to "scolding". | **Pragmatic Failure:** Failed to recognize financial ruin vs scolding. | |
| | **5** | Taglish | `Super nakakagigil yung deployment test... pero keribels lang kasi resolved na agad.` | Frustrating test, but manageable (`keribels`). | Defined `keribels` as meaning "stressful". | **Polarity Inversion:** Direct opposite meaning of slang term. | |
| | **9** | Spanglish | `Estoy super embarazada por el error que cometí...` | Embarrassment over a meeting blunder. | Claimed "super pregnant" is Spanish slang for "extreme anxiety". | **False Cognate Rationalization:** Invented a false linguistic rule to defend literal error. | |
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| --- |
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| ## Question 3: Path Forward & Proposed Solutions |
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| To bridge the performance gap in sub-6B open-weights models without increasing parameters: |
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| 1. **Vocabulary Expansion & Byte-Level Subword Merging:** Retrain base tokenizers on multi-dialectal social media corpora (X, Telegram, Reddit) to merge frequent Romanized and creole n-grams, reducing fragmentation penalties. |
| 2. **Participatory Community Data Curation:** Shift away from synthetic LLM-generated dialect corpora (which reproduce alignment biases). Recruit native speakers across dialect communities to curate authentic, pragmatically annotated conversational pairs. |
| 3. **Direct Preference Optimization (DPO) on Pragmatic Alignment:** Fine-tune models using LoRA adapters with preference pairs (**y_win**, **y_lose**), where **y_lose** represents literal translations or invented explanations, and **y_win** represents pragmatically accurate, context-aware responses. |
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| --- |
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| ## Repository Artifacts |
| * `multilingual_evaluation_results.json`: Raw evaluation logs and prompt outputs. |
| * `evaluation_notebook.ipynb`: Runnable Google Colab notebook used for benchmark execution. |
