39 citations · 39 across the 5 of their papers we have counts for
10 papers
Are Large Language Models Economically Viable for Industry Deployment?
Abdullah Mohammad, Sushant Kumar Ray, Pushkar Arora +5
Generative AI-powered by Large Language Models (LLMs)-is increasingly deployed in industry across healthcare decision support, financial analytics, enterprise retrieval, and conver…
They Said Memes Were Harmless-We Found the Ones That Hurt: Decoding Jokes, Symbols, and Cultural References
Sahil Tripathi, Gautam Siddharth Kashyap, Mehwish Nasim +3
Meme-based social abuse detection is challenging because harmful intent often relies on implicit cultural symbolism and subtle cross-modal incongruence. Prior approaches, from fusi…
Revealing the Truth with ConLLM for Detecting Multi-Modal Deepfakes
Gautam Siddharth Kashyap, Harsh Joshi, Niharika Jain +4
The rapid rise of deepfake technology poses a severe threat to social and political stability by enabling hyper-realistic synthetic media capable of manipulating public perception.…
Do Clinical Question Answering Systems Really Need Specialised Medical Fine Tuning?
Sushant Kumar Ray, Gautam Siddharth Kashyap, Sahil Tripathi +5
Clinical Question-Answering (CQA) industry systems are increasingly rely on Large Language Models (LLMs), yet their deployment is often guided by the assumption that domain-specifi…
Can We Predict Your Next Move Without Breaking Your Privacy?
Arpita Soni, Sahil Tripathi, Gautam Siddharth Kashyap +5
We propose FLLL3M--Federated Learning with Large Language Models for Mobility Modeling--a privacy-preserving framework for Next-Location Prediction (NxLP). By retaining user data l…
Truth, Trust, and Trouble: Medical AI on the Edge
Mohammad Anas Azeez, Rafiq Ali, Ebad Shabbir +4
Large Language Models (LLMs) hold significant promise for transforming digital health by enabling automated medical question answering. However, ensuring these models meet critical…