Papers by Xinyue Lou
MUCAR: Benchmarking Multilingual Cross-Modal Ambiguity Resolution for Multimodal Large Language Models (2025.emnlp-main)
Copied to clipboard
Xiaolong Wang, Zhaolu Kang, Wangyuxuan Zhai, Xinyue Lou, Yunghwei Lai, Ziyue Wang, Yawen Wang, Kaiyu Huang, Yile Wang, Peng Li, Yang Liu
| Challenge: | Existing multimodal benchmarks overlook linguistic and visual ambiguities, authors say . ambiguity resolution between modalities is lacking in multimodal large language models . |
| Approach: | They propose a benchmark to evaluate multimodal ambiguity resolution across multilingual and cross-modal scenarios. |
| Outcome: | a new benchmark evaluates multimodal ambiguity resolution across multilingual and cross-modal scenarios . the benchmark shows that MLLMs can resolve ambiguities in image-text alignment . however, existing benchmarks often overlook linguistic and visual ambiguties . |
When Helpers Become Hazards: A Benchmark for Analyzing Multimodal LLM-Powered Safety in Daily Life (2026.findings-acl)
Copied to clipboard
Xinyue Lou, Xu Jinan, Jingyi Yin, Xiaolong Wang, Zhaolu Kang, null Liaoyouwei, Yixuan Wang, Xiangyu Shi, Fengran Mo, SU Yao, Kaiyu Huang
| Challenge: | Safety impact of Multimodal Large Language Models (MLLMs) on human behavior is evaluated in this study. |
| Approach: | They propose a safety-warning-based evaluation framework that encourages models to provide clear and informative safety warnings, rather than generic refusals. |
| Outcome: | The proposed safety-warning-based evaluation framework encourages models to provide clear and informative safety warnings, rather than generic refusals. |
Think in Safety: Unveiling and Mitigating Safety Alignment Collapse in Multimodal Large Reasoning Model (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing Large Reasoning Models have demonstrated broad application potential, yet their safety and reliability remain critical concerns. |
| Approach: | They conduct a safety evaluation of 13 MLRMs across 5 benchmarks and examine their safety performance. |
| Outcome: | The proposed model improves safety on jailbreak and safety-awareness benchmarks. |