36 citations · 93 across the 25 of their papers we have counts for
4 papers · 1 filter
CLaRE-ty Amid Chaos: Quantifying Representational Entanglement to Predict Ripple Effects in LLM Editing
Manit Baser, Alperen Yildiz, Dinil Mon Divakaran +1
The static knowledge representations of large language models (LLMs) inevitably become outdated or incorrect over time. While model-editing techniques offer a promising solution by…
ThinkEval: Practical Evaluation of Knowledge Leakage in LLM Editing using Thought-based Knowledge Graphs
Manit Baser, Dinil Mon Divakaran, Mohan Gurusamy
Robust model-editing techniques are essential for deploying large language models (LLMs) in practical applications, as they enable cost-effective ways to deal with challenges such…
Markov Chain Monte Carlo-Based Machine Unlearning: Unlearning What Needs to be Forgotten
Quoc Phong Nguyen, Ryutaro Oikawa, Dinil Mon Divakaran +2
As the use of machine learning (ML) models is becoming increasingly popular in many real-world applications, there are practical challenges that need to be addressed for model main…
GEE: A Gradient-based Explainable Variational Autoencoder for Network Anomaly Detection
Quoc Phong Nguyen, Kar Wai Lim, Dinil Mon Divakaran +2
This paper looks into the problem of detecting network anomalies by analyzing NetFlow records. While many previous works have used statistical models and machine learning technique…