Papers by Zixin Tang
Learning to Write Rationally: How Information Is Distributed in Non-native Speakers’ Essays (2024.emnlp-main)
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| Challenge: | a study of second language learners with different native language backgrounds shows that people distribute information evenly in language production. |
| Approach: | They compare essays written by second language learners with different native language backgrounds to examine how they distribute information in non-native L2 production. |
| Outcome: | The authors found that writers with higher L2 proficiency can reduce uncertainty of language production while still conveying informative content. |
Using Contextually Aligned Online Reviews to Measure LLMs’ Performance Disparities Across Language Varieties (2025.naacl-short)
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| Challenge: | Of the world's 7,000 languages, sixty (60) million people speak British English, 23 million speak Taiwan Mandarin, and 10 million speak European Portuguese. |
| Approach: | They propose a contextually aligned dataset that captures comments in different languages from real-world scenarios. |
| Outcome: | The proposed approach shows that large language models underperform in Taiwan Mandarin in a sentiment analysis task. |
AEGIS: A Holistic Benchmark for Evaluating Forensic Analysis of AI-Generated Academic Images (2026.acl-long)
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Bo Zhang, Tzu-Yen Ma, Zichen Tang, Junpeng Ding, Zirui Wang, Yizhuo Zhao, Peilin Gao, Zijie Xi, Zixin Ding, Haiyang Sun, Haocheng Gao, Yuan Liu, Liangjia Wang, Yiling Huang, Yujie Wang, Yuyue Zhang, Ronghui Xi, Yuanze Li, Jiacheng Liu, Zhongjun Yang, Haihong E
| Challenge: | AEGIS examines whether current models can effectively audit AI-generated images in academic papers. |
| Approach: | They propose a holistic benchmark for forensic analysis of AI-Generated academic ImageS that reveals limitations in academic image forensics. |
| Outcome: | AEGIS compared with existing benchmarks on seven academic categories and features key advances in forensic analysis. |