---
dataset_info:
  features:
  - name: problem
    dtype: string
  - name: answer
    dtype: string
  - name: problem_image_1
    dtype: image
  - name: problem_image_2
    dtype: image
  - name: answer_image_1
    dtype: image
  - name: answer_image_2
    dtype: image
  splits:
  - name: LC219ALP038EV_problems
    num_bytes: 9123784.0
    num_examples: 50
  - name: LC004ALP000IV_problems
    num_bytes: 47181.0
    num_examples: 53
  - name: LC034ALP000EV_problems
    num_bytes: 7750282.0
    num_examples: 66
  - name: LC065ALP000IV_problems
    num_bytes: 3787936.0
    num_examples: 72
  - name: LC023ALP000EV_problems
    num_bytes: 106057.0
    num_examples: 189
  - name: LC021ALP000IV_problems
    num_bytes: 66166.0
    num_examples: 135
  - name: LC014ALP000EV_problems
    num_bytes: 32705564.0
    num_examples: 20
  - name: LC032ALP000IV_problems
    num_bytes: 6154578.0
    num_examples: 31
  - name: LC033ALP032EV_problems
    num_bytes: 2911677.0
    num_examples: 66
  - name: LC568ALP000EV_problems
    num_bytes: 2202668.0
    num_examples: 28
  - name: LC022ALP000EV_problems
    num_bytes: 984663.0
    num_examples: 137
  - name: LC023ALP000IV_problems
    num_bytes: 107414.0
    num_examples: 180
  - name: LC021ALP000EV_problems
    num_bytes: 58562.0
    num_examples: 135
  - name: LC065ALP000EV_problems
    num_bytes: 3307395.0
    num_examples: 72
  - name: LC014ALP000IV_problems
    num_bytes: 32706612.0
    num_examples: 20
  - name: LC004ALP000EV_problems
    num_bytes: 46230.0
    num_examples: 53
  - name: LC034ALP000IV_problems
    num_bytes: 5970070.0
    num_examples: 66
  - name: LC219ALP038IV_problems
    num_bytes: 10978459.0
    num_examples: 50
  - name: LC568ALP000IV_problems
    num_bytes: 1934458.0
    num_examples: 28
  - name: LC033ALP032IV_problems
    num_bytes: 2893013.0
    num_examples: 66
  - name: LC022ALP000IV_problems
    num_bytes: 936247.0
    num_examples: 137
  - name: LC032ALP000EV_problems
    num_bytes: 5782811.0
    num_examples: 31
  - name: LC003ALP100EV_problems
    num_bytes: 18540.0
    num_examples: 55
  - name: LC003ALP100IV_problems
    num_bytes: 20461.0
    num_examples: 55
  download_size: 68388663
  dataset_size: 130600828.0
configs:
- config_name: default
  data_files:
  - split: LC004ALP000IV_problems
    path: data/LC004ALP000IV_problems-*
  - split: LC034ALP000EV_problems
    path: data/LC034ALP000EV_problems-*
  - split: LC065ALP000IV_problems
    path: data/LC065ALP000IV_problems-*
  - split: LC023ALP000EV_problems
    path: data/LC023ALP000EV_problems-*
  - split: LC021ALP000IV_problems
    path: data/LC021ALP000IV_problems-*
  - split: LC014ALP000EV_problems
    path: data/LC014ALP000EV_problems-*
  - split: LC032ALP000IV_problems
    path: data/LC032ALP000IV_problems-*
  - split: LC033ALP032EV_problems
    path: data/LC033ALP032EV_problems-*
  - split: LC568ALP000EV_problems
    path: data/LC568ALP000EV_problems-*
  - split: LC219ALP038EV_problems
    path: data/LC219ALP038EV_problems-*
  - split: LC022ALP000EV_problems
    path: data/LC022ALP000EV_problems-*
  - split: LC023ALP000IV_problems
    path: data/LC023ALP000IV_problems-*
  - split: LC021ALP000EV_problems
    path: data/LC021ALP000EV_problems-*
  - split: LC065ALP000EV_problems
    path: data/LC065ALP000EV_problems-*
  - split: LC014ALP000IV_problems
    path: data/LC014ALP000IV_problems-*
  - split: LC004ALP000EV_problems
    path: data/LC004ALP000EV_problems-*
  - split: LC034ALP000IV_problems
    path: data/LC034ALP000IV_problems-*
  - split: LC219ALP038IV_problems
    path: data/LC219ALP038IV_problems-*
  - split: LC568ALP000IV_problems
    path: data/LC568ALP000IV_problems-*
  - split: LC033ALP032IV_problems
    path: data/LC033ALP032IV_problems-*
  - split: LC022ALP000IV_problems
    path: data/LC022ALP000IV_problems-*
  - split: LC032ALP000EV_problems
    path: data/LC032ALP000EV_problems-*
  - split: LC003ALP100EV_problems
    path: data/LC003ALP100EV_problems-*
  - split: LC003ALP100IV_problems
    path: data/LC003ALP100IV_problems-*
---

## IRLBench: A Multi-modal, Culturally Grounded, Parallel Irish-English Benchmark for Open-Ended LLM Reasoning Evaluation

### Overview
> Recent advances in Large Language Models (LLMs) have demonstrated promising knowledge and reasoning abilities, yet their performance in multilingual and low-resource settings remains underexplored. Existing benchmarks often exhibit cultural bias, restrict evaluation to text-only, rely on multiple-choice formats, and, more importantly, are limited for extremely low-resource languages. To address these gaps, we introduce IRLBench, presented in parallel English and Irish, which is considered definitely endangered by UNESCO. Our benchmark consists of 12 representative subjects developed from the 2024 Irish Leaving Certificate exams, enabling fine-grained analysis of model capabilities across domains. By framing the task as long-form generation and leveraging the official marking scheme, it does not only support a comprehensive evaluation of correctness but also language fidelity. Our extensive experiments of leading closed-source and open-source LLMs reveal a persistent performance gap between English and Irish and critical insights, in which models produce valid Irish responses less than 80% of the time, and answer correctly 55.8% of the time compared to 76.2% in English for the best-performing model. We release IRLBench and an accompanying evaluation codebase to enable future research on robust, culturally aware multilingual AI development.

### Usage
Load the dataset directly with the `datasets` library:
```python
from datasets import load_dataset
ds = load_dataset("ReliableAI/IRLBench")
```