Papers by Mukund Choudhary
Do LLMs model human linguistic variation? A case study in Hindi-English Verb code-mixing (2026.findings-eacl)
Copied to clipboard
| Challenge: | Existing large language models (LLMs) do not reliably classify verb language preferences to match native speaker judgments. |
| Approach: | They investigate whether large language models (LLMs) model linguistic variation by comparing Hindi-English verb code-mixing with English verb karna. |
| Outcome: | The proposed models do not reliably classify verb language preferences to match native speaker judgments, but with specific supervision, some models do predict human preference to an extent. |
Nanda Family: Open-Weights Generative Large Language Models for Hindi (2026.eacl-long)
Copied to clipboard
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. |