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Related Experiment Video

Updated: Jan 8, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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PROSHNO BINNASH: Contextual multi-label question answering dataset for low-resource NLP.

Rajib Khan1, Tanjim Taharat Aurpa2, Md Saidur Rahman1

  • 1Department of Computer Science and Engineering, International University of Business Agriculture and Technology, Uttara, Dhaka-1230, Bangladesh.

Data in Brief
|December 24, 2025
PubMed
Summary

PROSHNO BINNASH is a new Bangla question-answering dataset for low-resource natural language processing (NLP). This human-generated dataset aids in developing AI educational tools for Bengali speakers.

Keywords:
Automated learning systemsBanglaDatasetLow-resource NLPMulti-label classificationMulti-label question answering

Related Experiment Videos

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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Area of Science:

  • Natural Language Processing (NLP)
  • Artificial Intelligence (AI)
  • Computational Linguistics

Background:

  • Growing demand for automated learning tools in the Bangla language.
  • Limited availability of resources for low-resource natural language processing (NLP) tasks in Bangla.
  • Need for specialized datasets to advance AI-driven educational technology for Bengali speakers.

Purpose of the Study:

  • Introduce PROSHNO BINNASH, a novel contextual multi-label question answering dataset for Bangla NLP.
  • Address the gap in Bangla NLP resources for educational applications.
  • Facilitate the development of inclusive AI-powered educational tools for Bengali speakers.

Main Methods:

  • Curated 4069 entries from authentic Bengali texts, including literature and historical narratives.
  • Annotated each entry with context, question, answer, and multi-label classifications across ten categories.
  • Ensured accuracy and relevance through expert annotation of contexts, questions, and answers.

Main Results:

  • Developed PROSHNO BINNASH, a unique dataset featuring multi-label annotations for Bangla question answering.
  • Dataset includes ten categories: Sports, Health & Exercise, Literature, Prominent Person, Movie, Science, Liberation War, Politics, Artist, and History.
  • The dataset supports one-to-many relationships between questions and classes, reflecting educational content complexity.

Conclusions:

  • PROSHNO BINNASH fills a critical need in Bangla NLP research.
  • The dataset is valuable for education, Bangla analytics, multi-label classification, and academic chatbot development.
  • Contributes to advancing AI-powered educational technology for millions of Bengali speakers.