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20242026
most citedHuman Cognitive Benchmarks Reveal Foundational Visual Gaps in MLLMs

2 citations · 6 across the 15 of their papers we have counts for

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5 papers · 1 filter

cs.CL2024

On the Shortcut Learning in Multilingual Neural Machine Translation

Wenxuan Wang, Wenxiang Jiao, Jen-tse Huang +2

In this study, we revisit the commonly-cited off-target issue in multilingual neural machine translation (MNMT). By carefully designing experiments on different MNMT scenarios and…

cs.CL2024

Insight Over Sight: Exploring the Vision-Knowledge Conflicts in Multimodal LLMs

Xiaoyuan Liu, Wenxuan Wang, Youliang Yuan +4

This paper explores the problem of commonsense level vision-knowledge conflict in Multimodal Large Language Models (MLLMs), where visual information contradicts model's internal co…

cs.CL2024

Chain-of-Jailbreak Attack for Image Generation Models via Editing Step by Step

Wenxuan Wang, Kuiyi Gao, Youliang Yuan +5

Text-based image generation models, such as Stable Diffusion and DALL-E 3, hold significant potential in content creation and publishing workflows, making them the focus in recent…

cs.AI2024★ 1 cited

On the Resilience of LLM-Based Multi-Agent Collaboration with Faulty Agents

Jen-tse Huang, Jiaxu Zhou, Tailin Jin +6

Large language model-based multi-agent systems have shown great abilities across various tasks due to the collaboration of expert agents, each focusing on a specific domain. Howeve…

cs.CL2024

Refuse Whenever You Feel Unsafe: Improving Safety in LLMs via Decoupled Refusal Training

Youliang Yuan, Wenxiang Jiao, Wenxuan Wang +5

This study addresses a critical gap in safety tuning practices for Large Language Models (LLMs) by identifying and tackling a refusal position bias within safety tuning data, which…