Papers by Kumar Agrawal
Dense Retrieval with Quantity Comparison Intent (2025.findings-acl)
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| Challenge: | Existing sparse and dense retrieval systems fragment numerals and units that express quantities in arbitrary ways. |
| Approach: | They propose a dense retrieval system built around a density multi-vector index . they propose eliciting and exploiting quantities and associated comparison intents . |
| Outcome: | The proposed system is faster and more accurate than popular PLMs on two public and one proprietary e-commerce benchmarks. |
SCULPT: Systematic Tuning of Long Prompts (2025.acl-long)
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Shanu Kumar, Akhila Yesantarao Venkata, Shubhanshu Khandelwal, Bishal Santra, Parag Agrawal, Manish Gupta
| Challenge: | Existing methods for prompt optimization struggle with longer, more complex ones, often risking information loss and being sensitive to small perturbations. |
| Approach: | They propose a framework that treats prompt optimization as a hierarchical tree refinement problem and uses a Critic-Actor framework to generate reflections and apply actions to refine the prompt. |
| Outcome: | The proposed framework produces more stable and interpretable prompt modifications, ensuring better generalization across tasks. |
Navigating the Cultural Kaleidoscope: A Hitchhiker’s Guide to Sensitivity in Large Language Models (2025.naacl-long)
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Somnath Banerjee, Sayan Layek, Hari Shrawgi, Rajarshi Mandal, Avik Halder, Shanu Kumar, Sagnik Basu, Parag Agrawal, Rima Hazra, Animesh Mukherjee
| Challenge: | Cultural harm arises when LLMs misrepresent or normalize values, identities, and practices in ways that conflict with the norms of diverse cultural groups. |
| Approach: | They propose a cultural harm test dataset and a preference dataset to assess model outputs across different cultural contexts. |
| Outcome: | The proposed model improves model behavior significantly reducing the likelihood of generating culturally insensitive or harmful content. |
Attribute Diversity Determines the Systematicity Gap in VQA (2024.emnlp-main)
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| Challenge: | a systematicity gap exists between neural networks generalizing to new combinations of familiar concepts . conventionally trained neural networks struggle to generalize systematically . |
| Approach: | They propose to train a visual question answering model with CLEVR-HOPE as a diagnostic dataset to test this hypothesis. |
| Outcome: | The systematicity gap is reduced by increasing the diversity of training data, the authors show . the authors suggest that the more distinct attribute type combinations are seen during training, the more systematic the model will be. |
Enhancing Zero-shot Chain of Thought Prompting via Uncertainty-Guided Strategy Selection (2025.coling-main)
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Shanu Kumar, Saish Mendke, Karody Lubna Abdul Rahman, Santosh Kurasa, Parag Agrawal, Sandipan Dandapat
| Challenge: | Existing methods for chain-of-thought (CoT) prompting are limited by handcrafted demonstrations and trigger phrases are prone to inaccuracies. |
| Approach: | They propose a method that generates rationales using a trigger phrase to select effective demonstrations without accessing model parameters. |
| Outcome: | The proposed method outperforms existing methods across four reasoning benchmarks and is robust and scalable. |