BanglaNLG and BanglaT5: Benchmarks and Resources for Evaluating Low-Resource Natural Language Generation in Bangla

Challenge:	'BanglaNLG' is a comprehensive benchmark for evaluating natural language generation models in Bangla, a widely spoken yet low-resource language.
Approach:	They propose to aggregate six conditional text generation tasks under the BanglaNLG benchmark and introduce a new dataset on dialogue generation in the process.
Outcome:	The proposed model outperforms several multilingual models by 9% absolute gain and 32% relative gain on all of these tasks.

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Overview

Challenge

Approach

Outcome

BanglaBERT: Language Model Pretraining and Benchmarks for Low-Resource Language Understanding Evaluation in Bangla (2022.findings-naacl)

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Abhik Bhattacharjee, Tahmid Hasan, Wasi Ahmad, Kazi Samin Mubasshir, Md Saiful Islam, Anindya Iqbal, M. Sohel Rahman, Rifat Shahriyar

Language Models

Challenge:	Bangla is a widely spoken yet low-resource language in the NLP literature.
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IndoNLG: Benchmark and Resources for Evaluating Indonesian Natural Language Generation (2021.emnlp-main)

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Samuel Cahyawijaya, Genta Indra Winata, Bryan Wilie, Karissa Vincentio, Xiaohong Li, Adhiguna Kuncoro, Sebastian Ruder, Zhi Yuan Lim, Syafri Bahar, Masayu Khodra, Ayu Purwarianti, Pascale Fung

Language Models

Challenge:	Lack of publicly available NLG benchmarks for low-resource languages poses a challenge . authors show that IndoBART and IndoGPT achieve competitive performance on all tasks .
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BanglaByT5: Byte-Level Modelling for Bangla (2025.findings-emnlp)

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Pramit Bhattacharyya, Arnab Bhattacharya

Language Models

Challenge:	Large language models (LLMs) have achievedremarkable success across various natural lan-guage processing tasks.
Approach:	They propose a byte-level encoder-decoder model specifically tailored for Bangla.
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BanglaParaphrase: A High-Quality Bangla Paraphrase Dataset (2022.aacl-short)

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Text Generation

Challenge:	Bangla is considered a low resource language in terms of language processing.
Approach:	They propose a high-quality synthetic Bangla Paraphrase dataset curated by a novel filtering pipeline.
Outcome:	The proposed pipeline ensures quality by preserving both semantics and diversity, making it particularly useful to enhance other Bangla datasets.

BanglaTLit: A Benchmark Dataset for Back-Transliteration of Romanized Bangla (2024.findings-emnlp)

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Md Fahim, Fariha Shifat, Fabiha Haider, Deeparghya Barua, Md Sourove, Md Ishmam, Md Bhuiyan

Language Models

Challenge:	low-resource languages like Bangla are limited by the lack of datasets.
Approach:	They propose a large-scale transliteration dataset and a pre-training corpus on romanized Bangla.
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BanglaSTEM: A Parallel Corpus and Term-Weighted Evaluation for Technical Bangla-English Translation (2026.acl-srw)

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Kazi Reyazul Hasan, A. B. M. Alim Al Islam, Muhammad Abdullah Adnan

Multilinguality

Challenge:	Large language models excel at technical problem solving in English but struggle when questions are posed in Bangla.
Approach:	They propose a dataset of 5,000 Bangla-English sentence pairs to align technical terms . they use OCR to extract matching passages from bilingual textbooks .
Outcome:	The proposed pipeline extracts matching passages from bilingual textbooks and uses them to align sentences and mark technical terms.

TigerLLM - A Family of Bangla Large Language Models (2025.acl-short)

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Nishat Raihan, Marcos Zampieri

Language Models

Challenge:	linguistic disparity is particularly evident for Bangla, the 5th most spoken language . open-source Bangla LLMs have limited reproducibility and performance gaps .
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BenLLM-Eval: A Comprehensive Evaluation into the Potentials and Pitfalls of Large Language Models on Bengali NLP (2024.lrec-main)

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Mohsinul Kabir, Mohammed Saidul Islam, Md Tahmid Rahman Laskar, Mir Tafseer Nayeem, M Saiful Bari, Enamul Hoque

Language Models

Challenge:	Large Language Models (LLMs) have emerged as one of the most important breakthroughs in natural language processing.
Approach:	They propose to evaluate LLMs in Bengali to benchmark their performance . they select Bangla NLP tasks such as text summarization, question answering, paraphrasing .
Outcome:	The proposed model performs better in some tasks than current models, but in most tasks, it is poor .

TituLLMs: A Family of Bangla LLMs with Comprehensive Benchmarking (2025.findings-acl)

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Shahriar Kabir Nahin, Rabindra Nath Nandi, Sagor Sarker, Quazi Sarwar Muhtaseem, Md Kowsher, Apu Chandraw Shill, Md Ibrahim, Mehadi Hasan Menon, Tareq Al Muntasir, Firoj Alam

Multilinguality

Challenge:	Existing benchmarking datasets for Bangla LLMs are not available for all languages.
Approach:	They present TituLLMs, the first large pretrained Bangla LLMs, available in 1b and 3b parameter sizes.
Outcome:	The proposed model outperforms existing models in Bangla, but not always in the first place.

BanglaRQA: A Benchmark Dataset for Under-resourced Bangla Language Reading Comprehension-based Question Answering with Diverse Question-Answer Types (2022.findings-emnlp)

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Syed Mohammed Sartaj Ekram, Adham Arik Rahman, Md. Sajid Altaf, Mohammed Saidul Islam, Mehrab Mustafy Rahman, Md Mezbaur Rahman, Md Azam Hossain, Abu Raihan Mostofa Kamal

Question Answering

Challenge:	a lack of diverse and comprehensive question-answering datasets exists in under-resourced languages like Bangla.
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Outcome:	The proposed dataset shows that it performs well as a training resource in high-resource languages.