| 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. |
| Outcome: | The proposed model outperforms existing models in gen-erative and classification tasks and surpasses several multilingual and larger models. |
Similar Papers
TigerLLM - A Family of Bangla Large Language Models (2025.acl-short)
Copied to clipboard
| Challenge: | linguistic disparity is particularly evident for Bangla, the 5th most spoken language . open-source Bangla LLMs have limited reproducibility and performance gaps . |
| Approach: | They propose a family of Bangla LLMs that outperform open-source alternatives and benchmarks and establish a new benchmark for future Bangla language modeling. |
| Outcome: | The proposed models outperform existing models and outperformed proprietary models across six benchmarks. |
BanglaBERT: Language Model Pretraining and Benchmarks for Low-Resource Language Understanding Evaluation in Bangla (2022.findings-naacl)
Copied to clipboard
Abhik Bhattacharjee, Tahmid Hasan, Wasi Ahmad, Kazi Samin Mubasshir, Md Saiful Islam, Anindya Iqbal, M. Sohel Rahman, Rifat Shahriyar
| Challenge: | Bangla is a widely spoken yet low-resource language in the NLP literature. |
| Approach: | They propose a BERT-based natural language understanding model pretrainable in Bangla, a widely spoken yet low-resource language in the NLP literature. |
| Outcome: | The proposed model outperforms multilingual and monolingual models on four NLU tasks covering text classification, sequence labeling, and span prediction. |
BanglaTLit: A Benchmark Dataset for Back-Transliteration of Romanized Bangla (2024.findings-emnlp)
Copied to clipboard
| 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. |
| Outcome: | The proposed datasets show that the proposed methods can enrich romanized Bangla. |
BenLLM-Eval: A Comprehensive Evaluation into the Potentials and Pitfalls of Large Language Models on Bengali NLP (2024.lrec-main)
Copied to clipboard
Mohsinul Kabir, Mohammed Saidul Islam, Md Tahmid Rahman Laskar, Mir Tafseer Nayeem, M Saiful Bari, Enamul Hoque
| 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 . |
BanglaNLG and BanglaT5: Benchmarks and Resources for Evaluating Low-Resource Natural Language Generation in Bangla (2023.findings-eacl)
Copied to clipboard
| 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. |
TituLLMs: A Family of Bangla LLMs with Comprehensive Benchmarking (2025.findings-acl)
Copied to clipboard
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
| 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. |
BanglaSTEM: A Parallel Corpus and Term-Weighted Evaluation for Technical Bangla-English Translation (2026.acl-srw)
Copied to clipboard
| 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. |
BanglaParaphrase: A High-Quality Bangla Paraphrase Dataset (2022.aacl-short)
Copied to clipboard
| 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. |
BanglaAutoKG: Automatic Bangla Knowledge Graph Construction with Semantic Neural Graph Filtering (2024.lrec-main)
Copied to clipboard
| Challenge: | Bangla is underrepresented in KGs due to lack of comprehensive datasets, encoders, NER models, part-of-speech taggers, and lemmatizers. |
| Approach: | Bangla is underrepresented in KGs due to lack of comprehensive datasets, encoders, NER models, part-of-speech taggers, and lemmatizers. authors propose a framework that can automatically construct Bengali KG from any Bangla text. |
| Outcome: | The proposed framework can automatically construct Bengali KGs from any Bangla text. |
Leveraging LLMs for Bangla Grammar Error Correction: Error Categorization, Synthetic Data, and Model Evaluation (2025.findings-acl)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) perform exceedingly well in Natural Language Understanding tasks for many languages including English. |
| Approach: | They propose to use a rule-based noise injection method to create grammatically incorrect sentences . they categorize 12 error classes in Bangla and take a survey of native speakers . |
| Outcome: | The proposed method improves performance of LLMs in Bangla by 3-7 percentage points compared to zero-shot setting . human errors are still superior in error correction, the authors show . |