1 citations · 1 across the 9 of their papers we have counts for
9 papers
URAG: A Benchmark for Uncertainty Quantification in Retrieval-Augmented Large Language Models
Vinh Nguyen, Cuong Dang, Jiahao Zhang +6
Retrieval-Augmented Generation (RAG) has emerged as a widely adopted approach for enhancing LLMs in scenarios that demand extensive factual knowledge. However, current RAG evaluati…
What the "Spotless" Mind Remembers: How Knowledge Entanglement Shapes What Leaks After Unlearning in LLMs
Aakriti Shah, Yifan Hu, Thai Le
Unlearning in large language models (LLMs) is usually evaluated as whether an "unlearned" fact can be recovered. We instead ask whether a fact's structural entanglement with the re…
Harry Potter is Still Here! Probing Knowledge Leakage in Targeted Unlearned Large Language Models via Automated Adversarial Prompting
Bang Trinh Tran To, Thai Le
This work presents LURK (Latent UnleaRned Knowledge), a novel framework that probes for hidden retained knowledge in unlearned LLMs through adversarial suffix prompting. LURK autom…
Towards Robust and Accurate Stability Estimation of Local Surrogate Models in Text-based Explainable AI
Christopher Burger, Charles Walter, Thai Le +1
Recent work has investigated the concept of adversarial attacks on explainable AI (XAI) in the NLP domain with a focus on examining the vulnerability of local surrogate methods suc…
Beyond Individual Facts: Investigating Categorical Knowledge Locality of Taxonomy and Meronomy Concepts in GPT Models
Christopher Burger, Yifan Hu, Thai Le
The location of knowledge within Generative Pre-trained Transformer (GPT)-like models has seen extensive recent investigation. However, much of the work is focused towards determin…
The Effect of Similarity Measures on Accurate Stability Estimates for Local Surrogate Models in Text-based Explainable AI
Christopher Burger, Charles Walter, Thai Le
Recent work has investigated the vulnerability of local surrogate methods to adversarial perturbations on a machine learning (ML) model's inputs, where the explanation is manipulat…
A Curious Case of Searching for the Correlation between Training Data and Adversarial Robustness of Transformer Textual Models
Cuong Dang, Dung D. Le, Thai Le
Existing works have shown that fine-tuned textual transformer models achieve state-of-the-art prediction performances but are also vulnerable to adversarial text perturbations. Tra…
RMDM: A Multilabel Fakenews Dataset for Vietnamese Evidence Verification
Hai-Long Nguyen, Thi-Kieu-Trang Pham, Thai-Son Le +3
In this study, we present a novel and challenging multilabel Vietnamese dataset (RMDM) designed to assess the performance of large language models (LLMs), in verifying electronic i…
Are Your Explanations Reliable? Investigating the Stability of LIME in Explaining Text Classifiers by Marrying XAI and Adversarial Attack
Christopher Burger, Lingwei Chen, Thai Le
LIME has emerged as one of the most commonly referenced tools in explainable AI (XAI) frameworks that is integrated into critical machine learning applications--e.g., healthcare an…