arXiv:2607.05614v1 Announce Type: cross Abstract: Document comprehension is a challenging yet impactful task for Multimodal Large Language Models, especially as these systems see growing adoption in real-world, human-centric applications. However, this adoption is limited for low-resource languages such as Bangla due to the scarcity of high-quality annotated data. To address this gap, we introduce BaFCo, a benchmark dataset for Bangla form comprehension with a focus on Document Layout Analysis (DLA) and Key Information Extraction (KIE). BaFCo curates 200 multi-page complex Bangladeshi governme
The increasing adoption of Multimodal Large Language Models (LLMs) in real-world applications highlights the urgent need for robust data, especially for under-resourced languages like Bangla, to extend their utility.
This benchmark addresses a critical gap in data scarcity for low-resource languages, enabling more inclusive and geographically diverse AI applications and potentially accelerating AI development beyond dominant linguistic contexts.
The availability of BaFCo provides a standardized dataset for evaluating document understanding in Bangla, paving the way for more sophisticated LLM deployments in South Asia and for future multilingual AI advancements.
- · Bangla-speaking regions
- · Multilingual LLM developers
- · AI data annotation services
- · Government services in Bangladesh
- · AI models reliant solely on high-resource languages
- · Organizations without localized AI strategies
Improved performance of LLMs in document understanding for Bangla, enabling more accurate processing of local administrative and business documents.
Increased investment and development of AI applications tailored for the Bangladeshi market and other low-resource language communities.
Reduced digital divide for Bangla speakers, potentially leading to greater economic and social equity supported by localized AI services.
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Read at arXiv cs.AI