5 papers
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…
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…
The Confidence Paradox: Can LLM Know When It's Wrong
Sahil Tripathi, Md Tabrez Nafis, Imran Hussain +1
Document Visual Question Answering (DocVQA) models often produce overconfident or ethically misaligned responses, especially under uncertainty. Existing models like LayoutLMv3, UDO…
MAGIC-Enhanced Keyword Prompting for Zero-Shot Audio Captioning with CLIP Models
Vijay Govindarajan, Pratik Patel, Sahil Tripathi +2
Automated Audio Captioning (AAC) generates captions for audio clips but faces challenges due to limited datasets compared to image captioning. To overcome this, we propose the zero…
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…