1 citations · 3 across the 10 of their papers we have counts for
10 papers
Does Playing it Safe Count as Faithfulness? Reassessing LVLM Hallucination Mitigation Methods
Mehrdad Fazli, Sina Mansouri, Mohit Marvania +1
Recent inference-time hallucination mitigation methods for large vision-language models (LVLMs) report strong gains on hallucination benchmarks. However, it remains unclear whether…
Confidence-Orchestrated Self-Evolution against Uncertain LLM Feedback
Bowen Wei, Nan Wang, Yuqing Zhou +2
Self-evolving large language models (LLMs) learn by generating their own training tasks and solutions, reducing reliance on human-curated supervision. However, in many reasoning do…
A Logical-Rule Autoencoder for Interpretable Recommendations
Jinhao Pan, Bowen Wei, Ziwei Zhu
Most deep learning recommendation models operate as black boxes, relying on latent representations that obscure their decision process. This lack of intrinsic interpretability rais…
ClawSafety: "Safe" LLMs, Unsafe Agents
Bowen Wei, Yunbei Zhang, Jinhao Pan +5
Personal AI agents like OpenClaw run with elevated privileges on users' local machines, where a single successful prompt injection can leak credentials, redirect financial transact…
Context-Aware Decoding for Faithful Vision-Language Generation
Mehrdad Fazli, Bowen Wei, Ziwei Zhu
Hallucinations, generating responses inconsistent with the visual input, remain a critical limitation of large vision-language models (LVLMs), especially in open-ended tasks such a…
CORTEX: Collaborative LLM Agents for High-Stakes Alert Triage
Bowen Wei, Yuan Shen Tay, Howard Liu +4
Security Operations Centers (SOCs) are overwhelmed by tens of thousands of daily alerts, with only a small fraction corresponding to genuine attacks. This overload creates alert fa…
Learning to Explain: Prototype-Based Surrogate Models for LLM Classification
Bowen Wei, Mehrdad Fazli, Ziwei Zhu
Large language models (LLMs) have demonstrated impressive performance on natural language tasks, but their decision-making processes remain largely opaque. Existing explanation met…
Mitigating Hallucination in Large Vision-Language Models via Adaptive Attention Calibration
Mehrdad Fazli, Bowen Wei, Ahmet Sari +1
Large vision-language models (LVLMs) achieve impressive performance on multimodal tasks but often suffer from hallucination, and confidently describe objects or attributes not pres…
Advancing Interpretability in Text Classification through Prototype Learning
Bowen Wei, Ziwei Zhu
Deep neural networks have achieved remarkable performance in various text-based tasks but often lack interpretability, making them less suitable for applications where transparency…
Interpretable Classification via a Rule Network with Selective Logical Operators
Bowen Wei, Ziwei Zhu
We introduce the Rule Network with Selective Logical Operators (RNS), a novel neural architecture that employs \textbf{selective logical operators} to adaptively choose between AND…