# Bengali Text Classification: An Evaluation of Large Language Model Approaches

arXiv: https://arxiv.org/abs/2601.12132 · HTML: https://mlsanity.com/paper/2601.12132/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 26 references that are in the map.
- First posted on arXiv 17 Jan 2026.

## Builds on (most cited first, 4 of 4)

- [Classification Benchmarks for Under-resourced Bengali Language based on Multichannel Convolutional-LSTM Network](https://mlsanity.com/paper/2004.07807/) (2020)
- [TELEClass: Taxonomy Enrichment and LLM-Enhanced Hierarchical Text Classification with Minimal Supervision](https://mlsanity.com/paper/2403.00165/) (2024)
- [Leveraging Large Language Models for Topic Classification in the Domain of Public Affairs](https://mlsanity.com/paper/2306.02864/) (2023)
- [Bangla Text Classification using Transformers](https://mlsanity.com/paper/2011.04446/) (2020)

## Built on by (most cited first, 1 of 1)

- [Surpassing Scale by Efficiency: A Compact 135M Parameter Foundational LLM Natively Adapted for the Bangla Language](https://mlsanity.com/paper/2606.16383/) (2026)

## Abstract (arXiv)

Bengali text classification is a Significant task in natural language processing (NLP), where text is categorized into predefined labels. Unlike English, Bengali faces challenges due to the lack of extensive annotated datasets and pre-trained language models. This study explores the effectiveness of large language models (LLMs) in classifying Bengali newspaper articles. The dataset used, obtained from Kaggle, consists of articles from Prothom Alo, a major Bangladeshi newspaper. Three instruction-tuned LLMs LLaMA 3.1 8B Instruct, LLaMA 3.2 3B Instruct, and Qwen 2.5 7B Instruct were evaluated for this task under the same classification framework. Among the evaluated models, Qwen 2.5 achieved the highest classification accuracy of 72%, showing particular strength in the "Sports" category. In comparison, LLaMA 3.1 and LLaMA 3.2 attained accuracies of 53% and 56%, respectively. The findings highlight the effectiveness of LLMs in Bengali text classification, despite the scarcity of resources for Bengali NLP. Future research will focus on exploring additional models, addressing class imbalance issues, and refining fine-tuning approaches to improve classification performance.