---
language:
- en
- lg
size_categories:
- n<1K
tags:
- luganda
- education
- benchmark
- literacy
- low-resource-language
pretty_name: Luganda Linguistic Knowledge (LLK) Benchmark
configs:
- config_name: english
  data_files:
  - split: train
    path: english/*
- config_name: luganda
  data_files:
  - split: train
    path: luganda/*
---
# Luganda Linguistic Knowledge (LLK) Benchmark

Tests whether the model actually **knows** the Luganda language rules it is supposed to teach. Structured around CEFR levels with 75% of questions at foundational levels (A1–B1), heavily weighted toward Morphology & Concord (30%) and Syntax (25%) given Luganda's 12-noun-class agreement system. Includes C1–C2 stress tests for cultural context and advanced grammar. 100 mixed questions per language: multiple-choice (51), short-form (47), and true/false (2).

- **Repository:** [https://github.com/AI-for-Education/luganda-linguistic-benchmarks](https://github.com/AI-for-Education/luganda-linguistic-benchmarks)

## Configurations

This dataset has two language configurations:

- `english` — questions written in English (about Luganda)
- `luganda` — same questions translated into Luganda

## Columns

| Column | Description |
|---|---|
| `id` | Stable question identifier (e.g. `LUG_RET_001`) |
| `category` | Linguistic category (e.g. `Phonics & Orthography`, `Morphology & Concord`) |
| `question` | Question text (options inlined for multiple-choice items) |
| `answer_type` | `multiple_choice`, `short_form`, or `true_false` |
| `expected_answers` | List of acceptable answers |
| `rubric_id` | Scoring rubric (e.g. `multiple_choice`, `exact_match`) |
| `source_ref` | References to linguistic sources (e.g. *Handbook of Luganda*) |
| `framework_tag` | SVR (Simple View of Reading) component being assessed |
| `linguistic_feature` | Specific linguistic feature targeted |
| `proficiency_level` | CEFR level (A1, A2, B1, B2, C1, C2) |

## Source & methodology

Built by [AI for Education](https://huggingface.co/AI-for-Education)
as part of the Small Language Model finetuning project with [Crane AI Labs](https://huggingface.co/CraneAILabs). See the source repository for the
full evaluation harness, scoring code, and reproducibility notes.