2 citations · 2 across the 3 of their papers we have counts for
6 papers
Rarity of Singletons Matters & Supplementary Materials
Pragatheeswaran Vipulanandan, Kamal Premaratne, Dilip Sarkar
Estimating missing-mass and unseen-species counts from finite samples relies heavily on rare-event occupancy statistics, particularly singletons. In small-sample regimes, these sta…
Embedding Graph Kernel Entropy (EGKE): Fine-grained Hallucination Detection for LLMs from Embeddings
Dilrangi Sankalpana, Pragatheeswaran Vipulanandan, Uthayasanker Thayasivam +1
Hallucination detection is critical for ensuring the reliability of Large Language Models (LLMs) in real-world deployments. While recent works, such as Semantic Entropy (SE) and Ke…
Do LLMs Understand Context? A Knowledge Graph-Based Evaluation Framework
Subavarshana Arumugam, Mamta Nallaretnam, Kithuni Wickramasinghe +4
While large language models (LLMs) have achieved remarkable linguistic capabilities, a profound question lingers at their core: do these models truly comprehend context or simply e…
Evaluation of Contextual Understanding in Large Language Models
Subavarshana Arumugam, Mamta Nallaretnam, Kithuni Wickramasinghe +4
Large Language Models (LLMs) demonstrate impressive performance across diverse NLP tasks, yet their ability to exhibit genuine contextual understanding remains uncertain. Tradition…
A Graph Neural Network Model for Real-Time Gesture Recognition Based on sEMG Signals
Pragatheeswaran Vipulanandan, Kamal Premaratne, Manohar Murthi
For seemless control of advanced hand prostheses and augmented reality, accurate and immediate hand gestures recognition is essential. Surface electromyography (sEMG) signals obtai…
Semantic Uncertainty Quantification of Hallucinations in LLMs: A Quantum Tensor Network Based Method
Pragatheeswaran Vipulanandan, Kamal Premaratne, Dilip Sarkar
Large language models (LLMs) exhibit strong generative capabilities but remain vulnerable to confabulations, fluent yet unreliable outputs that vary arbitrarily even under identica…