BLUCK: A Benchmark Dataset for Bengali Linguistic Understanding and Cultural Knowledge

TL;DR

BLUCK is a new benchmark dataset for evaluating large language models' understanding of Bengali language and culture, highlighting current models' strengths and weaknesses in this mid-resource language.

Contribution

It introduces BLUCK, the first MCQ-based benchmark focused on Bengali culture, history, and linguistics, and evaluates multiple LLMs on this dataset.

Findings

Models perform reasonably well but struggle with Bengali phonetics.

Current LLMs' performance on Bengali is lower than on English.

BLUCK highlights Bengali as a mid-resource language.

Abstract

In this work, we introduce BLUCK, a new dataset designed to measure the performance of Large Language Models (LLMs) in Bengali linguistic understanding and cultural knowledge. Our dataset comprises 2366 multiple-choice questions (MCQs) carefully curated from compiled collections of several college and job level examinations and spans 23 categories covering knowledge on Bangladesh's culture and history and Bengali linguistics. We benchmarked BLUCK using 6 proprietary and 3 open-source LLMs - including GPT-4o, Claude-3.5-Sonnet, Gemini-1.5-Pro, Llama-3.3-70B-Instruct, and DeepSeekV3. Our results show that while these models perform reasonably well overall, they, however, struggles in some areas of Bengali phonetics. Although current LLMs' performance on Bengali cultural and linguistic contexts is still not comparable to that of mainstream languages like English, our results indicate Bengali's status as a mid-resource language. Importantly, BLUCK is also the first MCQ-based evaluation benchmark that is centered around native Bengali culture, history, and linguistics.