2 citations · 2 across the 12 of their papers we have counts for
12 papers
Accuracy is Not Enough: A Divergence-Based Approach to Evaluate Fidelity Loss in Quantized LLMs
Shahzeb Qamar, Lorenz Sparrenberg, Christian Bauckhage +5
Deployment of Large Language Models (LLMs) on memory-constrained edge devices relies heavily on aggressive post-training quantization. However, evaluating these models is largely b…
The Illusion of Equivalency: Statistical Characterization of Quantization Effects in LLMs
Baha Rababah, Shahzeb Qamar, Lorenz Sparrenberg +4
Post-Training Quantization has become widely used to compress large language models to make them deployable on resource-constrained devices. However, the evaluation of quantization…
Human Decision-Making with Persuasive and Narrative LLM Explanations
Laura R. Marusich, Mary Grace Kozuch Dhooghe, Jonathan Z. Bakdash +1
Large language models (LLMs) have the potential to aid and improve human decision-making in classification tasks, not only by providing fairly accurate predictions, but also in the…
CASPIAN: Online Detection and Attribution of Cascade Attacks in LLM Multi-Agent Systems via Cross-Channel Causal Monitoring
Kavana Venkatesh, Jafar Isbarov, Saad Amin +2
Cascade attacks in LLM multi-agent systems (MAS) arise when adversarial influence propagates across agents and leads to escalated system-level failures through complex agent intera…
PARALLAX: Separating Genuine Hallucination Detection from Benchmark Construction Artifacts
Khizar Hussain, Murat Kantarcioglu
Large language models (LLMs) hallucinate with confidence: their outputs can be fluent, authoritative, and simply wrong. In medical, legal, and scientific applications this failure…
CourtGuard: A Model-Agnostic Framework for Zero-Shot Policy Adaptation in LLM Safety
Umid Suleymanov, Rufiz Bayramov, Suad Gafarli +4
Current safety mechanisms for Large Language Models (LLMs) rely heavily on static, fine-tuned classifiers that suffer from adaptation rigidity, the inability to enforce new governa…
Ask ChatGPT: Caveats and Mitigations for Individual Users of AI Chatbots
Chengen Wang, Murat Kantarcioglu
As ChatGPT and other Large Language Model (LLM)-based AI chatbots become increasingly integrated into individuals' daily lives, important research questions arise. What concerns an…
Graph Generative Models Evaluation with Masked Autoencoder
Chengen Wang, Murat Kantarcioglu
In recent years, numerous graph generative models (GGMs) have been proposed. However, evaluating these models remains a considerable challenge, primarily due to the difficulty in e…
A Review of DeepSeek Models' Key Innovative Techniques
Chengen Wang, Murat Kantarcioglu
DeepSeek-V3 and DeepSeek-R1 are leading open-source Large Language Models (LLMs) for general-purpose tasks and reasoning, achieving performance comparable to state-of-the-art close…
A Systematic Evaluation of Generative Models on Tabular Transportation Data
Chengen Wang, Alvaro Cardenas, Gurcan Comert +1
The sharing of large-scale transportation data is beneficial for transportation planning and policymaking. However, it also raises significant security and privacy concerns, as the…
How to Backdoor Consistency Models?
Chengen Wang, Murat Kantarcioglu
Consistency models are a new class of models that generate images by directly mapping noise to data, allowing for one-step generation and significantly accelerating the sampling pr…
Using AI Uncertainty Quantification to Improve Human Decision-Making
Laura R. Marusich, Jonathan Z. Bakdash, Yan Zhou +1
AI Uncertainty Quantification (UQ) has the potential to improve human decision-making beyond AI predictions alone by providing additional probabilistic information to users. The ma…