---
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)

## Question 1: Critical Blind Spot & Capability Gap

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). 

Current frontier small language models (0.6B–6B parameters) exhibit three systematic failure modes in these linguistic regimes:

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.

---

## Question 2: Systematic Evaluation Narrative (`google/gemma-2-2b-it`)

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. 

### Selected Empirical Results

| 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. |

---

## Question 3: Path Forward & Proposed Solutions

To bridge the performance gap in sub-6B open-weights models without increasing parameters:

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.

---

## Repository Artifacts
* `multilingual_evaluation_results.json`: Raw evaluation logs and prompt outputs.
* `evaluation_notebook.ipynb`: Runnable Google Colab notebook used for benchmark execution.