Papers by Onkar Pandit
CNN for Text-Based Multiple Choice Question Answering (P18-2)
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| Challenge: | Existing models for text-based multiple choice question answering are based on a text. |
| Approach: | They propose a Convolutional Neural Network (CNN) model for text-based multiple choice question answering where questions are based on a particular article. |
| Outcome: | The proposed model outperforms several baseline models on the SciQ and TQA datasets. |
Probing for Bridging Inference in Transformer Language Models (2021.naacl-main)
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| Challenge: | Pre-trained transformer language models are capable of bridging inference, but they lack the commonsense knowledge to capture syntactic information. |
| Approach: | They investigate whether pre-trained transformer language models capture bridging inference . they use a masked token prediction task to investigate attention heads in BERT . |
| Outcome: | The proposed model significantly captures bridging inference, the authors show . the distance between anaphor-antecedent and context plays an important role in the inference . |
Nanda Family: Open-Weights Generative Large Language Models for Hindi (2026.eacl-long)
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Aaryamonvikram Singh, Debopriyo Banerjee, Dhruv Sahnan, Monojit Choudhury, Shivam Chauhan, Rocktim Jyoti Das, Xudong Han, Haonan Li, Alok Anil Jadhav, Utkarsh Agarwal, Mukund Choudhary, Fajri Koto, Junaid Hamid Bhat, Awantika Shukla, Samujjwal Ghosh, Samta Kamboj, Onkar Pandit, Lalit Pradhan, Rahul Pal, Sunil Kumar Sahu, Parvez Mullah, Ali El Filali, Zainul Abedien Ahmed Quraishi, Neha Sengupta, Gokulakrishnan Ramakrishnan, Rituraj Joshi, Gurpreet Gosal, Avraham Sheinin, Natalia Vassilieva, Preslav Nakov
| Challenge: | Large language models remain predominantly English-centric, which limits their utility for underrepresented languages. |
| Approach: | They propose to extend Llama’s vocabulary with 20% Hindi-specific tokens, thus halving Hindi tokenization fertility while preserving English efficiency. |
| Outcome: | The proposed models outperform open-weight models of comparable size on a 65B-token corpus and bilingual instruction and safety alignment on . a culturally grounded dataset. |