activity
20232026
most citedHow Far Are We on the Decision-Making of LLMs? Evaluating LLMs' Gaming Ability in Multi-Agent Environments

9 citations · 28 across the 19 of their papers we have counts for

collaborators
Showing cs.CLShow all

11 papers · 1 filter

cs.CL2026

SHAPE: Unifying Safety, Helpfulness and Pedagogy for Educational LLMs

Sihang Zhao, Kangrui Yu, Youliang Yuan +2

Large Language Models (LLMs) have been widely explored in educational scenarios. We identify a critical vulnerability in current educational LLMs, pedagogical jailbreaks, where stu…

cs.CL2025

Curing Miracle Steps in LLM Mathematical Reasoning with Rubric Rewards

Youliang Yuan, Qiuyang Mang, Jingbang Chen +7

In this paper, we observe that current models are susceptible to reward hacking, leading to a substantial overestimation of a model's reasoning ability. This is evidenced by a high…

cs.CL2025

Towards Evaluating Proactive Risk Awareness of Multimodal Language Models

Youliang Yuan, Wenxiang Jiao, Yuejin Xie +5

Human safety awareness gaps often prevent the timely recognition of everyday risks. In solving this problem, a proactive safety artificial intelligence (AI) system would work bette…

cs.CL2025

Can't See the Forest for the Trees: Benchmarking Multimodal Safety Awareness for Multimodal LLMs

Wenxuan Wang, Xiaoyuan Liu, Kuiyi Gao +5

Multimodal Large Language Models (MLLMs) have expanded the capabilities of traditional language models by enabling interaction through both text and images. However, ensuring the s…

cs.CL2024★ 1 cited

Difficult Task Yes but Simple Task No: Unveiling the Laziness in Multimodal LLMs

Sihang Zhao, Youliang Yuan, Xiaoying Tang +1

Multimodal Large Language Models (MLLMs) demonstrate a strong understanding of the real world and can even handle complex tasks. However, they still fail on some straightforward vi…

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.CL2024

Learning to Ask: When LLM Agents Meet Unclear Instruction

Wenxuan Wang, Juluan Shi, Zixuan Ling +7

Equipped with the capability to call functions, modern large language models (LLMs) can leverage external tools for addressing a range of tasks unattainable through language skills…

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…

cs.CL2023★ 6 cited

Who is ChatGPT? Benchmarking LLMs' Psychological Portrayal Using PsychoBench

Jen-tse Huang, Wenxuan Wang, Eric John Li +6

Large Language Models (LLMs) have recently showcased their remarkable capacities, not only in natural language processing tasks but also across diverse domains such as clinical med…

cs.CL2023★ 4 cited

All Languages Matter: On the Multilingual Safety of Large Language Models

Wenxuan Wang, Zhaopeng Tu, Chang Chen +4

Safety lies at the core of developing and deploying large language models (LLMs). However, previous safety benchmarks only concern the safety in one language, e.g. the majority lan…