Papers by Yiran Luo
Event-Radar: Event-driven Multi-View Learning for Multimodal Fake News Detection (2024.acl-long)
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| Challenge: | Existing methods for detecting multimedia fake news have demonstrated excellent results . however, addressing event-level inconsistency and learning from poor-quality news remains a challenge . |
| Approach: | They propose an Event-diven fake news detection framework that integrates visual manipulation, textual emotion and multimodal inconsistency at event-level for fake news identification. |
| Outcome: | The proposed framework performs well on three large-scale fake news detection benchmarks. |
Lost in Translation? Translation Errors and Challenges for Fair Assessment of Text-to-Image Models on Multilingual Concepts (2024.naacl-short)
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| Challenge: | With growth in the popularity of text-to-image models has come interest in assessing their multilingual capabilities, including multilingual accessibility. |
| Approach: | They propose to correct translation errors in a concept list translated to seven languages and compare the outputs of the benchmark to those conditioned on the old. |
| Outcome: | The proposed benchmark contains translation errors in Spanish, Japanese, and Chinese. |
DynamicNER: A Dynamic, Multilingual, and Fine-Grained Dataset for LLM-based Named Entity Recognition (2025.emnlp-main)
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Hanjun Luo, Yingbin Jin, Yiran Wang, Xinfeng Li, Tong Shang, Xuecheng Liu, Ruizhe Chen, Kun Wang, Hanan Salam, Qingsong Wen, Zuozhu Liu
| Challenge: | Existing datasets designed for Named Entity Recognition methods are inadequate for LLMs. |
| Approach: | They propose a dataset that is multilingual and multi-granular and enables LLMs to be applied to Named Entity Recognition methods. |
| Outcome: | The proposed dataset is multilingual and multi-granular, covering 8 languages and 155 entity types, with corpora spanning a diverse range of domains. |
To Find Waldo You Need Contextual Cues: Debiasing Who’s Waldo (2022.acl-short)
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| Challenge: | Existing datasets for Person-centric visual grounding (PCVG) are biased and over-estimated . |
| Approach: | They propose to use a dataset for the Person-centric Visual Grounding task that requires pairing up a person’s name mentioned in a caption with a bounding box that points to the person in the image. |
| Outcome: | The proposed dataset offers a more practical benchmark for future improvements and better performance than the original dataset. |