Papers by Yuyue Zhao
UniRel: Unified Representation and Interaction for Joint Relational Triple Extraction (2022.emnlp-main)
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| Challenge: | Existing approaches to extract rich correlations between entities and relations are not fully exploited by existing methods. |
| Approach: | They propose to unify entities and relations by jointly encoding them within a concatenated natural language sequence and unify the modeling of interactions with a proposed Interaction Map. |
| Outcome: | The proposed method is more efficient and efficient than existing methods and can be scaled up to 2021. |
A + B: A General Generator-Reader Framework for Optimizing LLMs to Unleash Synergy Potential (2024.findings-acl)
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| Challenge: | Retrieval-Augmented Generation (RAG) is an effective solution to supplement necessary knowledge to large language models. |
| Approach: | They propose a "generate-then-read" pipeline to replace retrieval stage with generation from the LLM itself. |
| Outcome: | The proposed framework outperforms single models in the base and chat versions and addresses safety and helpfulness post-adaptation challenges. |
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. |
EvoWiki: Evaluating LLMs on Evolving Knowledge (2025.acl-long)
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Wei Tang, Yixin Cao, Yang Deng, Jiahao Ying, Bo Wang, Yizhe Yang, Yuyue Zhao, Qi Zhang, Xuanjing Huang, Yu-Gang Jiang, Yong Liao
| Challenge: | Existing knowledge evolution benchmarks are static and fail to capture the evolving nature of LLMs and knowledge. |
| Approach: | They propose an evolving dataset that categorizes information into stable, evolved, and uncharted states. |
| Outcome: | The proposed dataset is auto-updatable and enables evaluation of continuously changing knowledge and newly released LLMs. |