1 citations · 3 across the 10 of their papers we have counts for
12 papers
Learning from Self-Generated Responses: Reliability--Uncertainty Self-Distillation for Reasoning
Jinhao Pan, Bowen Wei, Yuqing Zhou +1
Reference solutions and question-specific rubrics are not always available to guide the teacher in self-distillation. A large language model's own solution attempts offer an altern…
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…
Knowing Bias, Doing Better: Mitigating Social Bias in LLMs via Know-Bias Neuron Enhancement
Jinhao Pan, Chahat Raj, Anjishnu Mukherjee +4
Large language models (LLMs) exhibit social biases that reinforce harmful stereotypes, limiting their safe deployment. Most existing debiasing methods adopt a suppressive paradigm…
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…
VIGNETTE: Socially Grounded Bias Evaluation for Vision-Language Models
Chahat Raj, Bowen Wei, Aylin Caliskan +2
While bias in large language models (LLMs) is well-studied, similar concerns in vision-language models (VLMs) have received comparatively less attention. Existing VLM bias studies…
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…