Conference Paper2026

Can Small LLMs Replace Fine-Tuned BERT for Bangla Natural Language Inference?

Istiaq Ahmed, Faiza Omar Arpita, Md Asif Bin Khaled, Ashraful Islam, M Ashraful Amin

2026 Third International Conference on Innovations in Cybersecurity and Data Science (ICICDS)

IEEE, pp. 853-859

DOI: 10.1109/icicds70526.2026.11604766URL

Keywords

  • small language models
  • Bengali language
  • natural language inference
  • fine-tuned BERT
  • multilingual language models

CCDS Authors

Istiaq Ahmed
Istiaq Ahmed

Istiaq Ahmed

  • Research Assistant, HCI Wing

Published at

CHI, HCII

MD Asif Bin Khaled
Md Asif Bin Khaled

MD Asif Bin Khaled

  • Co-Director, MIRA Wing

Published at

IEEE Internet of Things Journal

Ashraful Islam, PhD
Ashraful Islam

Ashraful Islam, PhD

  • Center Director, CCDS
  • Director, HCI Wing

Published at

CHI, UIST, UbiComp, IJCNN, ICIP, HCII, ICDMW, BLP, IEEE Internet of Things Journal, JMIR, JMIR Human Factors, JMIR Formative Research, IEEE Access, Smart Agricultural Technology, Diabetes Research and Clinical Practice, PLOS ONE

Prof. M Ashraful Amin, PhD
M Ashraful Amin, PhD

Prof. M Ashraful Amin, PhD

  • Founder Director, CCDS
  • Director, AI/ML, HCI & DS Wings

Published at

ACL, CHI, UbiComp, ECAI, WACV, PAKDD, IJCNN, ICPR, ICIP, HCII, CLEF, BLP, Scientific Reports, JMIR, IEEE Access, Smart Agricultural Technology, Frontiers in Computational Neuroscience, Diabetes Research and Clinical Practice, PLOS ONE

References

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  2. 2.Abhik Bhattacharjee, Tahmid Hasan, Wasi Ahmad, Kazi Samin Mubasshir, Md Saiful Islam, Anindya Iqbal, M. Sohel Rahman, Rifat Shahriyar. (2022). BanglaBERT: Language Model Pretraining and Benchmarks for Low-Resource Language Understanding Evaluation in Bangla. Findings of the Association for Computational Linguistics: NAACL 2022, 1318–1327[10.18653/v1/2022.findings-naacl.98]
  3. 3.Haoyang Huang, Tianyi Tang, Dongdong Zhang, Xin Zhao, Ting Song, Yan Xia, Furu Wei. (2023). Not All Languages Are Created Equal in LLMs: Improving Multilingual Capability by Cross-Lingual-Thought Prompting. , 12365–12394[10.18653/v1/2023.findings-emnlp.826]
  4. 4.Samuel Cahyawijaya, Holy Lovenia, Pascale Fung. (2024). LLMs Are Few-Shot In-Context Low-Resource Language Learners. , 405–433[10.18653/v1/2024.naacl-long.24]
  5. 5.Nishat Raihan, Marcos Zampieri. (2025). TigerLLM - A Family of Bangla Large Language Models. , 887–896[10.18653/v1/2025.acl-short.69]
  6. 6.Md. Arid Hasan, Shudipta Das, Afiyat Anjum, Firoj Alam, Anika Anjum, Avijit Sarker, Sheak Rashed Haider Noori. (2024). Zero- and Few-Shot Prompting with LLMs: A Comparative Study with Fine-tuned Models for Bangla Sentiment Analysis. , 17808–17818[10.63317/3edi4825svh3]
  7. 7.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. (2025). TituLLMs: A Family of Bangla LLMs with Comprehensive Benchmarking. , 24922–24940[10.18653/v1/2025.findings-acl.1279]
  8. 8.Md. Sajjad Hossain, Kawsar Ahmed, SUNY MD ASHRAF KHAN, Mohammed Moshiul Hoque. (2026). Exploring Large Language Models for Multitask Learning in Bengali Text Classification. , 92–104[10.63317/54y27enmr5e9]
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