7 papers
With Argus Eyes: Assessing Retrieval Gaps via Uncertainty Scoring to Detect and Remedy Retrieval Blind Spots
Zeinab Sadat Taghavi, Ali Modarressi, Hinrich Schutze +1
Reliable retrieval-augmented generation (RAG) systems depend fundamentally on the retriever's ability to find relevant information. We show that neural retrievers used in RAG syste…
DISTIL: Data-Free Inversion of Suspicious Trojan Inputs via Latent Diffusion
Hossein Mirzaei, Zeinab Taghavi, Sepehr Rezaee +3
Deep neural networks have demonstrated remarkable success across numerous tasks, yet they remain vulnerable to Trojan (backdoor) attacks, raising serious concerns about their safet…
ImpliRet: Benchmarking the Implicit Fact Retrieval Challenge
Zeinab Sadat Taghavi, Ali Modarressi, Yunpu Ma +1
Retrieval systems are central to many NLP pipelines, but often rely on surface-level cues such as keyword overlap and lexical semantic similarity. To evaluate retrieval beyond thes…
A Contrastive Teacher-Student Framework for Novelty Detection under Style Shifts
Hossein Mirzaei, Mojtaba Nafez, Moein Madadi +12
There have been several efforts to improve Novelty Detection (ND) performance. However, ND methods often suffer significant performance drops under minor distribution shifts caused…
Scanning Trojaned Models Using Out-of-Distribution Samples
Hossein Mirzaei, Ali Ansari, Bahar Dibaei Nia +10
Scanning for trojan (backdoor) in deep neural networks is crucial due to their significant real-world applications. There has been an increasing focus on developing effective gener…
Killing it with Zero-Shot: Adversarially Robust Novelty Detection
Hossein Mirzaei, Mohammad Jafari, Hamid Reza Dehbashi +3
Novelty Detection (ND) plays a crucial role in machine learning by identifying new or unseen data during model inference. This capability is especially important for the safe and r…
Backdooring Outlier Detection Methods: A Novel Attack Approach
ZeinabSadat Taghavi, Hossein Mirzaei
There have been several efforts in backdoor attacks, but these have primarily focused on the closed-set performance of classifiers (i.e., classification). This has left a gap in ad…