3 citations · 4 across the 10 of their papers we have counts for
7 papers · 1 filter
LLMs Learn to Evade Latent Monitors from Prior Feedback Alone
Hugo Lyons Keenan, Christopher Leckie, Sarah Erfani
Latent space monitors aim to detect undesired behaviors in LLM agents by inspecting an agent's internal activations rather than its outputs. However, interactive monitoring creates…
VisER: Visual Evidence and Reliance for Object Hallucination Detection in LVLMs
Afsaneh Hasanebrahimi, Hanxun Huang, Christopher Leckie +1
Object hallucination remains a persistent reliability issue in large vision-language models, where generated object mentions may sound plausible but lack visual grounding. Recent t…
GeoDetect: Geometric Adversarial Detection for VLPs
Afsaneh Hasanebrahimi, Hanxun Huang, Christopher Leckie +2
Vision-language pre-trained models (VLPs) are widely used in real-world applications. However, they remain vulnerable to adversarial attacks. Although adversarial detection methods…
Semantic Robustness Certification for Vision-Language Models
Peiyu Yang, Paul Montague, Feng Liu +4
Vision-language models (VLMs) are now widely used in downstream tasks. However, real-world applications often expose VLMs to distribution shifts induced by semantic variation (e.g.…
Density-Aware Translation of Spurious Correlations in Zero-Shot VLMs
Afsaneh Hasanebrahimi, Hanxun Huang, Christopher Leckie +1
Vision-Language models (VLMs), such as CLIP, achieve powerful zero-shot classification. However, their predictions remain sensitive to spurious correlations, where contextual cues…
TRACER: Persistent Regularization for Robust Multimodal Finetuning
Hesam Asadollahzadeh, Feng Liu, Christopher Leckie +1
Mainstream strategies for finetuning pretrained multimodal models often degrade out-of-distribution (OOD) robustness, a phenomenon known as catastrophic forgetting. In this paper,…
Mechanistic Anomaly Detection via Functional Attribution
Hugo Lyons Keenan, Christopher Leckie, Sarah Erfani
We can often verify the correctness of neural network outputs using ground truth labels, but we cannot reliably determine whether the output was produced by normal or anomalous int…