25 citations · 25 across the 7 of their papers we have counts for
8 papers
DreamGuard: Efficient Runtime Guardrail for LLM Agents via Risk-Aware World Model
Wenhao Lin, Chenyu Yu, Xingwei Lin +6
As large language model (LLM) agents increasingly invoke external tools and interact with real-world systems, unsafe actions may cause irreversible consequences on external states,…
Interaction-Consistent Object Removal via MLLM-Based Reasoning
Ching-Kai Huang, Wen-Chieh Lin, Yan-Cen Lee
Image-based object removal often erases only the named target, leaving behind interaction evidence that renders the result semantically inconsistent. We formalize this problem as I…
ICON: Intent-Context Coupling for Efficient Multi-Turn Jailbreak Attack
Xingwei Lin, Wenhao Lin, Sicong Cao +4
Multi-turn jailbreak attacks have emerged as a critical threat to Large Language Models (LLMs), bypassing safety mechanisms by progressively constructing adversarial contexts from…
SEDA: A Self-Adapted Entity-Centric Data Augmentation for Boosting Gird-based Discontinuous NER Models
Wen-Fang Su, Hsiao-Wei Chou, Wen-Yang Lin
Named Entity Recognition (NER) is a critical task in natural language processing, yet it remains particularly challenging for discontinuous entities. The primary difficulty lies in…
What Kind of Visual Tokens Do We Need? Training-free Visual Token Pruning for Multi-modal Large Language Models from the Perspective of Graph
Yutao Jiang, Qiong Wu, Wenhao Lin +2
Recent Multimodal Large Language Models(MLLMs) often use a large number of visual tokens to compensate their visual shortcoming, leading to excessive computation and obvious visual…
Accelerating Multimodal Large Language Models via Dynamic Visual-Token Exit and the Empirical Findings
Qiong Wu, Wenhao Lin, Yiyi Zhou +4
The excessive use of visual tokens in existing Multimoal Large Language Models (MLLMs) often exhibits obvious redundancy and brings in prohibitively expensive computation. To gain…
Fit and Prune: Fast and Training-free Visual Token Pruning for Multi-modal Large Language Models
Weihao Ye, Qiong Wu, Wenhao Lin +1
Recent progress in Multimodal Large Language Models(MLLMs) often use large image tokens to compensate the visual shortcoming of MLLMs, which not only exhibits obvious redundancy bu…
iEnhancer-ELM: improve enhancer identification by extracting position-related multiscale contextual information based on enhancer language models
Jiahao Li, Zhourun Wu, Wenhao Lin +4
Motivation: Enhancers are important cis-regulatory elements that regulate a wide range of biological functions and enhance the transcription of target genes. Although many feature…