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

TL;DR

IRLBench is a culturally grounded, bilingual Irish-English benchmark for evaluating large language models' reasoning and language fidelity in low-resource, multilingual settings, revealing significant performance gaps especially in Irish.

Contribution

Introduces IRLBench, a novel Irish-English benchmark based on Irish exam data, enabling detailed evaluation of LLMs in low-resource, culturally specific contexts.

Findings

Models perform less than 80% valid Irish responses

Best models answer correctly 55.8% in Irish vs. 76.2% in English

Benchmark supports comprehensive, culturally aware multilingual evaluation

Abstract

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, 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 (https://huggingface.co/datasets/ReliableAI/IRLBench) and an accompanying evaluation codebase (https://github.com/ReML-AI/IRLBench) to enable future research on robust, culturally aware multilingual AI development.