Papers by Zelin Wang
Understanding and Improving the Robustness of Terminology Constraints in Neural Machine Translation (2023.acl-long)
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| Challenge: | Existing terminology constraint test sets are blind to this issue due to oversimplified settings . PH methods retain high constraint accuracy but lower translation quality . |
| Approach: | They propose a method that replaces terminology terms with ordered labels . placeholder methods are better at retaining high constraint accuracy but lower translation quality . |
| Outcome: | The proposed method achieves high accuracy and translation quality regardless of the number or length of constraints. |
AgentAsk: Multi-Agent Systems Need to Ask (2026.acl-long)
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Bohan Lin, Kuo Yang, Zelin Tan, Yingchuan Lai, Chen Zhang, Guibin Zhang, Xinlei Yu, Miao Yu, Xu Wang, Yudong Zhang, Yang Wang
| Challenge: | Multi-agent systems fail to consistently outperform strong single-a agent baselines due to error propagation at inter-aggent message handoffs. |
| Approach: | They propose an edge-level error taxonomy that identifies four main errors in multi-agent interactions as data gaps, signal corruption, referential drift and capacity gaps as primary sources of failure. |
| Outcome: | The proposed module outperforms existing systems on five benchmarks and is architecture-agnostic. |
Commonsense Knowledge Salience Evaluation with a Benchmark Dataset in E-commerce (2022.findings-emnlp)
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| Challenge: | Existing models rank statements solely by confidence scores, and there is no information about which ones are salient from a human perspective. |
| Approach: | They propose a task where a model is required to learn whether a triple is salient . they propose supervised salience evaluation using a new Benchmark dataset . |
| Outcome: | The proposed task is based on a new Benchmark dataset of salience evaluation in e-commerce . it shows that saliency evaluation is hard, where models perform poorly on evaluation set . |
Mnemis: Dual-Route Retrieval on Hierarchical Graphs for Long-Term LLM Memory (2026.acl-long)
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Zihao Tang, Xin Yu, Ziyu Xiao, Zengxuan Wen, Zelin Li, Jiaxi Zhou, Hualei Wang, Haohua Wang, Haizhen Huang, Weiwei Deng, Feng Sun, Qi Zhang
| Challenge: | Existing methods for retrieving historical messages are based on similarity-based mechanisms. |
| Approach: | They propose a system that integrates System-1 similarity search with a complementary System-2 mechanism, termed Global Selection. |
| Outcome: | The proposed framework achieves state-of-the-art on long-term memory benchmarks and 93.9 on LoCoMo and 91.6 on LongMemEval-S. |
LLMEval-Med: A Real-world Clinical Benchmark for Medical LLMs with Physician Validation (2025.findings-emnlp)
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Ming Zhang, Yujiong Shen, Zelin Li, Huayu Sha, Binze Hu, Yuhui Wang, Chenhao Huang, Shichun Liu, Jingqi Tong, Changhao Jiang, Mingxu Chai, Zhiheng Xi, Shihan Dou, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Current medical benchmarks have limitations in question design, data sources and evaluation methods. |
| Approach: | They propose a new benchmark covering five core medical areas . it includes 2,996 questions created from real-world electronic health records . |
| Outcome: | The proposed model covers five core medical areas and includes 2,996 questions created from real-world electronic health records and expert-designed clinical scenarios. |
From Bottom to Top: Extending the Potential of Parameter Efficient Fine-Tuning (2024.emnlp-main)
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| Challenge: | Existing methods to fine-tune large language models primarily focus on the interaction between different layers, ignoring the fact that different layers store different information. |
| Approach: | They propose a Parameter Efficient Fine-Tuning method which freeze pre-trained parameters and fine-tunes only a few task-specific parameters. |
| Outcome: | The proposed methods reduce parameter count to nearly half by omitting fine-tuning in the middle layers. |
Transferable and Efficient: Unifying Dynamic Multi-Domain Product Categorization (2023.acl-industry)
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| Challenge: | e-commerce platforms are encountering increasingly complex product categorization scenarios . multiple business domains correspond to different category taxonomies, with different depths and distinct literal expressions of category names. |
| Approach: | They propose a taxonomy-agnostic framework that calculates semantic relatedness between product titles and category names in the vector space. |
| Outcome: | The proposed framework outperforms strong baselineson three dynamic multi-domain product categorization tasks. |