Papers by Raghvendra Kumar
DRISHTIKON: A Multimodal Multilingual Benchmark for Testing Language Models’ Understanding on Indian Culture (2025.emnlp-main)
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Arijit Maji, Raghvendra Kumar, Akash Ghosh, null Anushka, Nemil Shah, Abhilekh Borah, Vanshika Shah, Nishant Mishra, Sriparna Saha
| Challenge: | DRISHTIKON is a first-of-its-kind multimodal and multilingual benchmark centered exclusively on Indian culture. |
| Approach: | They evaluate a wide range of vision-language models across zero-shot and chain-of-thought settings and use them to evaluate cultural understanding of generative AI systems. |
| Outcome: | The DRISHTIKON dataset covers 15 languages, all states and union territories, and incorporating over 64,000 aligned text-image pairs. |
Poetry in Pixels: Prompt Tuning for Poem Image Generation via Diffusion Models (2025.coling-main)
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Sofia Jamil, Bollampalli Areen Reddy, Raghvendra Kumar, Sriparna Saha, Joseph K. J, Koustava Goswami
| Challenge: | Poems are a distinct form of literature, with meanings that transcend beyond the literal words. |
| Approach: | They propose a framework to generate images that visually represent the meanings of poems using prompt tuning and a PoeKey algorithm to extract emotions, visual elements, and themes from poems. |
| Outcome: | The proposed framework generates images that visually represent the meanings of poems and their images. |
SANSKRITI: A Comprehensive Benchmark for Evaluating Language Models’ Knowledge of Indian Culture (2025.findings-acl)
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| Challenge: | Language models excel in syntactic and semantic analysis, while small language models struggle in region-specific contexts. |
| Approach: | They evaluate SANSKRITI on leading Large Language Models, Indic Language Model, and Small Language Model (SLM) it covers 16 key attributes of Indian culture including rituals and ceremonies, history, tourism, cuisine, dance and music, costume, language, art, festivals, religion, medicine, transport, sports, nightlife and personalities. |
| Outcome: | The SANSKRITI dataset covers 16 attributes of Indian culture . it reveals that many models struggle in region-specific contexts . |
From Fragments to Facts: A Curriculum-Driven DPO Approach for Generating Hindi News Veracity Explanations (2026.findings-acl)
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| Challenge: | DeFactoX integrates Direct Preference Optimization (DPO) with Curriculum learning to align machine-generated explanations with human reasoning. |
| Approach: | They propose a framework that integrates Direct Preference Optimization (DPO) with Curriculum learning to align machine-generated explanations with human reasoning. |
| Outcome: | The proposed framework combines Direct Preference Optimization (DPO) with Curriculum learning to align machine-generated explanations with human reasoning. |
BhashaSutra: A Task-Centric Unified Survey of Indian NLP Datasets, Corpora, and Resources (2026.acl-long)
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| Challenge: | Existing reviews focus on a few high-resource languages or embed Indian languages within broad multilingual settings, limiting coverage of low-resourced and culturally diverse varieties. |
| Approach: | They present a unified survey of Indian NLP resources, covering 200+ datasets, 50+ benchmarks, and 100+ models, tools, and systems across text, speech, multimodal, and culturally grounded tasks. |
| Outcome: | The proposed survey covers 200+ datasets, 50+ benchmarks, and 100+ models, tools, and systems across text, speech, multimodal, and culturally grounded tasks. |
COSMMIC: Comment-Sensitive Multimodal Multilingual Indian Corpus for Summarization and Headline Generation (2025.acl-long)
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Raghvendra Kumar, Mohammed Salman S A, Aryan Sahu, Tridib Nandi, Pragathi Y P, Sriparna Saha, Jose G Moreno
| Challenge: | COSMMIC is a multimodal, multilingual dataset featuring nine major Indian languages. |
| Approach: | They propose a multimodal, multilingual multimodal multimodal dataset that integrates text, images and user feedback to enhance summarization. |
| Outcome: | The proposed dataset is based on 4,959 article-image pairs and 24,484 reader comments with ground-truth summaries available in all included languages. |