Papers by Dhruv Agarwal
Bring Your Own KG: Self-Supervised Program Synthesis for Zero-Shot KGQA (2024.findings-naacl)
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| Challenge: | a universal question-answering system that can operate on any knowledge graph is presented . previous work that relied on training data to query structured data stores is unrealistic . |
| Approach: | They propose a universal question-answering system that can operate on any knowledge graph . they use an LLM-backed symbolic agent to generate query-program exemplars . |
| Outcome: | The proposed system outperforms state-of-the-art model on domain-specific KGs by 7.08 F1 . the proposed system can be ready to use within a day, the authors show . |
Entity Linking via Explicit Mention-Mention Coreference Modeling (2022.naacl-main)
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| Challenge: | Using a learning approach for entity mentions is a key component of modern entity linking systems for both candidate generation and making linking predictions. |
| Approach: | They propose a training approach that builds minimum spanning arborescences over mentions and entities to explicitly model mention coreference relationships. |
| Outcome: | The proposed approach improves candidate generation recall and link accuracy on the biomedical dataset and on MedMentions, setting a new SOTA result in linking accuracy. |
Machine Reading Comprehension using Case-based Reasoning (2023.findings-emnlp)
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Dung Thai, Dhruv Agarwal, Mudit Chaudhary, Wenlong Zhao, Rajarshi Das, Jay-Yoon Lee, Hannaneh Hajishirzi, Manzil Zaheer, Andrew McCallum
| Challenge: | Current state-of-the-art machine readers do not support case-based reasoning . |
| Approach: | They propose a method that extracts a set of similar cases from a nonparametric memory and then predicts an answer by selecting the span in the test context that is most similar to the contextualized representations of answers. |
| Outcome: | The proposed method outperforms baselines on NaturalQuestions and NewsQA by 11.5 and 8.4 EM. |
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. |