# Bangla-Bayanno: A 52K-Pair Bengali Visual Question Answering Dataset with LLM-Assisted Translation Refinement

## TL;DR

Bangla-Bayanno is a high-quality, open-source Bengali VQA dataset with 52,650 question-answer pairs, created using LLM-assisted translation to improve translation quality and support low-resource multimodal AI research.

## Contribution

This paper introduces Bangla-Bayanno, the first large-scale Bengali VQA dataset refined with LLM-assisted translation to ensure high quality and inclusivity in low-resource language AI research.

## Key findings

- The dataset contains 52,650 question-answer pairs.
- Questions are categorized into nominal, quantitative, and polar types.
- Provides a comprehensive benchmark for Bengali multimodal AI.

## Abstract

In this paper, we introduce Bangla-Bayanno, an open-ended Visual Question Answering (VQA) Dataset in Bangla, a widely used, low-resource language in multimodal AI research. The majority of existing datasets are either manually annotated with an emphasis on a specific domain, query type, or answer type or are constrained by niche answer formats. In order to mitigate human-induced errors and guarantee lucidity, we implemented a multilingual LLM-assisted translation refinement pipeline. This dataset overcomes the issues of low-quality translations from multilingual sources. The dataset comprises 52,650 question-answer pairs across 4750+ images. Questions are classified into three distinct answer types: nominal (short descriptive), quantitative (numeric), and polar (yes/no). Bangla-Bayanno provides the most comprehensive open-source, high-quality VQA benchmark in Bangla, aiming to advance research in low-resource multimodal learning and facilitate the development of more inclusive AI systems.

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Source: https://tomesphere.com/paper/2508.19887