14 citations · 18 across the 6 of their papers we have counts for
8 papers
Overalignment in Frontier LLMs: An Empirical Study of Sycophantic Behaviour in Healthcare
Clément Christophe, Wadood Mohammed Abdul, Prateek Munjal +3
As LLMs are increasingly integrated into clinical workflows, their tendency for sycophancy, prioritizing user agreement over factual accuracy, poses significant risks to patient sa…
Do Instruction-Tuned Models Always Perform Better Than Base Models? Evidence from Math and Domain-Shifted Benchmarks
Prateek Munjal, Clement Christophe, Ronnie Rajan +1
Instruction finetuning is standard practice for improving LLM performance, yet it remains unclear whether it enhances reasoning or merely induces surface-level pattern matching. We…
Beyond Metrics: A Critical Analysis of the Variability in Large Language Model Evaluation Frameworks
Marco AF Pimentel, Clément Christophe, Tathagata Raha +3
As large language models (LLMs) continue to evolve, the need for robust and standardized evaluation benchmarks becomes paramount. Evaluating the performance of these models is a co…
Med42 -- Evaluating Fine-Tuning Strategies for Medical LLMs: Full-Parameter vs. Parameter-Efficient Approaches
Clément Christophe, Praveen K Kanithi, Prateek Munjal +13
This study presents a comprehensive analysis and comparison of two predominant fine-tuning methodologies - full-parameter fine-tuning and parameter-efficient tuning - within the co…
FAIRS -- Soft Focus Generator and Attention for Robust Object Segmentation from Extreme Points
Ahmed H. Shahin, Prateek Munjal, Ling Shao +1
Semantic segmentation from user inputs has been actively studied to facilitate interactive segmentation for data annotation and other applications. Recent studies have shown that e…
Towards Robust and Reproducible Active Learning Using Neural Networks
Prateek Munjal, Nasir Hayat, Munawar Hayat +2
Active learning (AL) is a promising ML paradigm that has the potential to parse through large unlabeled data and help reduce annotation cost in domains where labeling data can be p…
Implicit Discriminator in Variational Autoencoder
Prateek Munjal, Akanksha Paul, Narayanan C. Krishnan
Recently generative models have focused on combining the advantages of variational autoencoders (VAE) and generative adversarial networks (GAN) for good reconstruction and generati…
Semantically Aligned Bias Reducing Zero Shot Learning
Akanksha Paul, Narayanan C. Krishnan, Prateek Munjal
Zero shot learning (ZSL) aims to recognize unseen classes by exploiting semantic relationships between seen and unseen classes. Two major problems faced by ZSL algorithms are the h…