17 citations · 17 across the 5 of their papers we have counts for
5 papers
LLM is Not All You Need: A Systematic Evaluation of ML vs. Foundation Models for text and image based Medical Classification
Meet Raval, Tejul Pandit, Dhvani Upadhyay
The combination of multimodal Vision-Language Models (VLMs) and Large Language Models (LLMs) opens up new possibilities for medical classification. This work offers a rigorous, uni…
The Evolution of Reranking Models in Information Retrieval: From Heuristic Methods to Large Language Models
Tejul Pandit, Sakshi Mahendru, Meet Raval +1
Reranking is a critical stage in contemporary information retrieval (IR) systems, improving the relevance of the user-presented final results by honing initial candidate sets. This…
Multi-Domain ABSA Conversation Dataset Generation via LLMs for Real-World Evaluation and Model Comparison
Tejul Pandit, Meet Raval, Dhvani Upadhyay
Aspect-Based Sentiment Analysis (ABSA) offers granular insights into opinions but often suffers from the scarcity of diverse, labeled datasets that reflect real-world conversationa…
SecureNet: A Comparative Study of DeBERTa and Large Language Models for Phishing Detection
Sakshi Mahendru, Tejul Pandit
Phishing, whether through email, SMS, or malicious websites, poses a major threat to organizations by using social engineering to trick users into revealing sensitive information.…
Venn Diagram Prompting : Accelerating Comprehension with Scaffolding Effect
Sakshi Mahendru, Tejul Pandit
We introduce Venn Diagram (VD) Prompting, an innovative prompting technique which allows Large Language Models (LLMs) to combine and synthesize information across complex, diverse…