5 citations · 20 across the 31 of their papers we have counts for
12 papers · 1 filter
Charts Are Not Images: On the Challenges of Scientific Chart Editing
Shawn Li, Ryan Rossi, Sungchul Kim +5
Generative models, such as diffusion and autoregressive approaches, have demonstrated impressive capabilities in editing natural images. However, applying these tools to scientific…
LLM-Powered Text-Attributed Graph Anomaly Detection via Retrieval-Augmented Reasoning
Haoyan Xu, Ruizhi Qian, Zhengtao Yao +10
Anomaly detection on attributed graphs plays an essential role in applications such as fraud detection, intrusion monitoring, and misinformation analysis. However, text-attributed…
Mitigating Hallucinations in Large Language Models via Causal Reasoning
Yuangang Li, Yiqing Shen, Yi Nian +7
Large language models (LLMs) exhibit logically inconsistent hallucinations that appear coherent yet violate reasoning principles, with recent research suggesting an inverse relatio…
M3OOD: Automatic Selection of Multimodal OOD Detectors
Yuehan Qin, Li Li, Defu Cao +3
Out-of-distribution (OOD) robustness is a critical challenge for modern machine learning systems, particularly as they increasingly operate in multimodal settings involving inputs…
CoAct-1: Computer-using Multi-Agent System with Coding Actions
Linxin Song, Yutong Dai, Viraj Prabhu +9
Autonomous agents that operate computers via Graphical User Interfaces (GUIs) often struggle with efficiency and reliability on complex, long-horizon tasks. While augmenting these…
A Personalized Conversational Benchmark: Towards Simulating Personalized Conversations
Li Li, Peilin Cai, Ryan A. Rossi +21
We present PersonaConvBench, a large-scale benchmark for evaluating personalized reasoning and generation in multi-turn conversations with large language models (LLMs). Unlike exis…
Don't Let It Hallucinate: Premise Verification via Retrieval-Augmented Logical Reasoning
Yuehan Qin, Shawn Li, Yi Nian +3
Large language models (LLMs) have shown substantial capacity for generating fluent, contextually appropriate responses. However, they can produce hallucinated outputs, especially w…
JailDAM: Jailbreak Detection with Adaptive Memory for Vision-Language Model
Yi Nian, Shenzhe Zhu, Yuehan Qin +4
Multimodal large language models (MLLMs) excel in vision-language tasks but also pose significant risks of generating harmful content, particularly through jailbreak attacks. Jailb…
Treble Counterfactual VLMs: A Causal Approach to Hallucination
Shawn Li, Jiashu Qu, Yuxiao Zhou +3
Vision-Language Models (VLMs) have advanced multi-modal tasks like image captioning, visual question answering, and reasoning. However, they often generate hallucinated outputs inc…
Secure On-Device Video OOD Detection Without Backpropagation
Shawn Li, Peilin Cai, Yuxiao Zhou +7
Out-of-Distribution (OOD) detection is critical for ensuring the reliability of machine learning models in safety-critical applications such as autonomous driving and medical diagn…
DecAlign: Hierarchical Cross-Modal Alignment for Decoupled Multimodal Representation Learning
Chengxuan Qian, Shuo Xing, Shawn Li +2
Multimodal representation learning aims to capture both shared and complementary semantic information across multiple modalities. However, the intrinsic heterogeneity of diverse mo…
Generative AI in Transportation Planning: A Survey
Longchao Da, Tiejin Chen, Zhuoheng Li +14
The integration of generative artificial intelligence (GenAI) into transportation planning has the potential to revolutionize tasks such as demand forecasting, infrastructure desig…