1 citations · 1 across the 4 of their papers we have counts for
5 papers
Think-Reflect-Revise: A Policy-Guided Reflective Framework for Safety Alignment in Large Vision Language Models
Fenghua Weng, Chaochao Lu, Xia Hu +2
As multimodal reasoning improves the overall capabilities of Large Vision Language Models (LVLMs), recent studies have begun to explore safety-oriented reasoning, aiming to enhance…
Beyond Jailbreaks: Revealing Stealthier and Broader LLM Security Risks Stemming from Alignment Failures
Yukai Zhou, Sibei Yang, Wenjie Wang
Large language models (LLMs) are increasingly deployed in real-world applications, raising concerns about their security. While jailbreak attacks highlight failures under overtly h…
OmniGeo: Towards a Multimodal Large Language Models for Geospatial Artificial Intelligence
Long Yuan, Fengran Mo, Kaiyu Huang +6
The rapid advancement of multimodal large language models (LLMs) has opened new frontiers in artificial intelligence, enabling the integration of diverse large-scale data types suc…
Adversary-Aware DPO: Enhancing Safety Alignment in Vision Language Models via Adversarial Training
Fenghua Weng, Jian Lou, Jun Feng +2
Safety alignment is critical in pre-training large language models (LLMs) to generate responses aligned with human values and refuse harmful queries. Unlike LLM, the current safety…
DELMAN: Dynamic Defense Against Large Language Model Jailbreaking with Model Editing
Yi Wang, Fenghua Weng, Sibei Yang +3
Large Language Models (LLMs) are widely applied in decision making, but their deployment is threatened by jailbreak attacks, where adversarial users manipulate model behavior to by…