18 citations · 18 across the 9 of their papers we have counts for
9 papers
PPE-Bench: A Benchmark for Evaluating MLLM Unlearning under Private-Public Entanglement
Xianren Zhang, Delvin Ce Zhang, Dongwon Lee +1
Multimodal Large Language Models (MLLMs) have shown strong capabilities, but they may memorize private information from web data, raising privacy concerns. Machine unlearning offer…
GraphSkill: Documentation-Guided Hierarchical Retrieval-Augmented Coding for Complex Graph Reasoning
Fali Wang, Chenglin Weng, Xianren Zhang +3
The growing demand for automated graph algorithm reasoning has attracted increasing attention in the large language model (LLM) community. Recent LLM-based graph reasoning methods…
MEVER: Multi-Modal and Explainable Claim Verification with Graph-based Evidence Retrieval
Delvin Ce Zhang, Suhan Cui, Zhelin Chu +2
Verifying the truthfulness of claims usually requires joint multi-modal reasoning over both textual and visual evidence, such as analyzing both textual caption and chart image for…
A Functionality-Grounded Benchmark for Evaluating Web Agents in E-commerce Domains
Xianren Zhang, Shreyas Prasad, Di Wang +4
Web agents have shown great promise in performing many tasks on ecommerce website. To assess their capabilities, several benchmarks have been introduced. However, current benchmark…
SUA: Stealthy Multimodal Large Language Model Unlearning Attack
Xianren Zhang, Hui Liu, Delvin Ce Zhang +4
Multimodal Large Language Models (MLLMs) trained on massive data may memorize sensitive personal information and photos, posing serious privacy risks. To mitigate this, MLLM unlear…
Image Corruption-Inspired Membership Inference Attacks against Large Vision-Language Models
Zongyu Wu, Minhua Lin, Zhiwei Zhang +4
Large vision-language models (LVLMs) have demonstrated outstanding performance in many downstream tasks. However, LVLMs are trained on large-scale datasets, which can pose privacy…
A Comprehensive Survey of Small Language Models in the Era of Large Language Models: Techniques, Enhancements, Applications, Collaboration with LLMs, and Trustworthiness
Fali Wang, Zhiwei Zhang, Xianren Zhang +11
Large language models (LLMs) have demonstrated emergent abilities in text generation, question answering, and reasoning, facilitating various tasks and domains. Despite their profi…
Divide-Verify-Refine: Can LLMs Self-Align with Complex Instructions?
Xianren Zhang, Xianfeng Tang, Hui Liu +4
Recent studies show LLMs struggle with complex instructions involving multiple constraints (e.g., length, format, sentiment). Existing works address this issue by fine-tuning, whic…
Comprehensive Attribution: Inherently Explainable Vision Model with Feature Detector
Xianren Zhang, Dongwon Lee, Suhang Wang
As deep vision models' popularity rapidly increases, there is a growing emphasis on explanations for model predictions. The inherently explainable attribution method aims to enhanc…