Papers by Nipun Joshi
Revealing the Truth with ConLLM for Detecting Multi-Modal Deepfakes (2026.findings-eacl)
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Gautam Siddharth Kashyap, Harsh Joshi, Niharika Jain, Ebad Shabbir, Jiechao Gao, Nipun Joshi, Usman Naseem
| Challenge: | Existing methods for deepfake detection suffer from two limitations: modality fragmentation and shallow inter-modal reasoning. |
| Approach: | They propose a framework for multimodal deepfake detection that uses contrastive learning and large language models to mitigate modality fragmentation and refine embeddings to address shallow inter-modal reasoning. |
| Outcome: | ConLLM reduces audio deepfake EER by 50%, improves video accuracy by 8%, and achieves approximately 9% accuracy gains in audio-visual tasks. |
Do Clinical Question Answering Systems Really Need Specialised Medical Fine Tuning? (2026.eacl-industry)
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Sushant Kumar Ray, Gautam Siddharth Kashyap, Sahil Tripathi, Nipun Joshi, Vijay Govindarajan, Rafiq Ali, Jiechao Gao, Usman Naseem
| Challenge: | Clinical Question-Answering (CQA) industry systems rely on Large Language Models (LLMs). |
| Approach: | They propose a framework that applies alignment at inference time rather than through SFT to help CQA users achieve consistent reasoning. |
| Outcome: | MEDASSESS-X improves Accuracy, Factual Consistency and Safety by up to 50%. |