Papers by Rabindra Nath Nandi
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
| 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. |
BnTTS: Few-Shot Speaker Adaptation in Low-Resource Setting (2025.findings-naacl)
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Mohammad Jahid Ibna Basher, Md Kowsher, Md Saiful Islam, Rabindra Nath Nandi, Nusrat Jahan Prottasha, Mehadi Hasan Menon, Tareq Al Muntasir, Shammur Absar Chowdhury, Firoj Alam, Niloofar Yousefi, Ozlem Garibay
| Challenge: | Empirical evaluations in few-shot settings show that BnTTS significantly improves the naturalness, intelligibility, and speaker fidelity of synthesized Bangla speech. |
| Approach: | They propose to integrate Bangla into a multilingual TTS pipeline with modifications to account for the phonetic and linguistic characteristics of the language. |
| Outcome: | The proposed framework improves the naturalness, intelligibility, and speaker fidelity of synthesized Bangla speech compared to state-of-the-art systems. |
An Experimental Analysis on Evaluating Patent Citations (2024.emnlp-main)
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| Challenge: | Graph Neural Networks (GNNs)-based methods can predict patent citations using only patent text. |
| Approach: | They propose to use Graph Neural Networks to predict citations for patents based on their semantic similarities to generate a semantic graph of patents. |
| Outcome: | The proposed methods produce 94% recall for patents with high citations and outperform baselines. |