1 citations · 1 across the 11 of their papers we have counts for
11 papers
UXBench: Measuring the Actionability of LLM-Generated UX Critiques
Wenjie Wang, Yue Huang, Zipeng Ling +11
Large language models (LLMs) are increasingly deployed as UX judges that inspect interfaces, diagnose usability problems, and propose repairs. Yet no controlled benchmark measures…
SpecAlign: Efficient Specification-Grounded Alignment of Large Language Models via Synthetic Data
Wenjie Wang, Yue Huang, Zhengqing Yuan +6
As large language models (LLMs) are increasingly deployed in real-world applications, alignment is no longer governed by a single universal notion of safety or helpfulness, but ins…
LiveAgentBench: Comprehensive Benchmarking of Agentic Systems Across 104 Real-World Challenges
Hao Li, Huan Wang, Jinjie Gu +3
As large language models grow more capable, general AI agents have become increasingly prevalent in practical applications. However, existing benchmarks face significant limitation…
Silent Sabotage During Fine-Tuning: Few-Shot Rationale Poisoning of Compact Medical LLMs
Jingyuan Xie, Wenjie Wang, Ji Wu +1
Supervised fine-tuning (SFT) is essential for the development of medical large language models (LLMs), yet prior poisoning studies have mainly focused on the detectable backdoor at…
Synthetic Interaction Data for Scalable Personalization in Large Language Models
Yuchen Ma, Yue Huang, Wenjie Wang +3
Personalized prompting offers large opportunities for deploying large language models (LLMs) to diverse users, yet existing prompt optimization methods primarily focus on task-leve…
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…
MM-BrowseComp: A Comprehensive Benchmark for Multimodal Browsing Agents
Shilong Li, Xingyuan Bu, Wenjie Wang +22
AI agents with advanced reasoning and tool-use capabilities have demonstrated impressive performance in web browsing for deep search. However, existing benchmarks such as BrowseCom…
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…