Papers by Zhenghao Zhou
MCTS: A Multi-Reference Chinese Text Simplification Dataset (2024.lrec-main)
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Ruining Chong, Luming Lu, Liner Yang, Jinran Nie, Zhenghao Liu, Shuo Wang, Shuhan Zhou, Yaoxin Li, Erhong Yang
| Challenge: | Existing studies on text simplification systems have focused on unsupervised methods due to the limited evaluation data in language and domain. |
| Approach: | They propose a Chinese text simplification dataset that provides a detailed analysis and an annotation process. |
| Outcome: | The proposed dataset evaluates the performance of unsupervised methods and advanced large language models. |
Is In-Context Learning a Type of Error-Driven Learning? Evidence from the Inverse Frequency Effect in Structural Priming (2025.naacl-long)
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| Challenge: | Recent pre-trained large language models have shown the capacity to perform in-context learning (ICL) this capability could provide a way to bridge the divide between language models and humans. |
| Approach: | They propose a new way of diagnosing whether ICL is error-driven learning . they simulated structural priming with ICL and found the effect was stronger . |
| Outcome: | The proposed method is based on the inverse frequency effect (IFE) phenomenon is similar to error-driven learning in large language models . |
MatPlotAgent: Method and Evaluation for LLM-Based Agentic Scientific Data Visualization (2024.findings-acl)
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Zhiyu Yang, Zihan Zhou, Shuo Wang, Xin Cong, Xu Han, Yukun Yan, Zhenghao Liu, Zhixing Tan, Pengyuan Liu, Dong Yu, Zhiyuan Liu, Xiaodong Shi, Maosong Sun
| Challenge: | Scientific data visualization is an essential process in research, but its use of large language models remains unexplored. |
| Approach: | They propose a model-agnostic LLM agent framework to automate scientific data visualization tasks. |
| Outcome: | The proposed framework improves performance of commercial and open-source models. |
Meaning Beyond Truth Conditions: Evaluating Discourse Level Understanding via Anaphora Accessibility (2025.acl-long)
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| Challenge: | Existing assessments of understanding at the lexical and sentence levels are limited to lexica and sentence level, but few of them target whether LLMs accurately represent and update states of natural language discourse. |
| Approach: | They propose anaphora accessibility as a diagnostic for assessing discourse understanding . they use a dataset inspired by theoretical research in dynamic semantics to evaluate human and LLM performance. |
| Outcome: | The proposed dataset shows that humans and LLMs align on some tasks and diverge on others. |
DocRED: A Large-Scale Document-Level Relation Extraction Dataset (P19-1)
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Yuan Yao, Deming Ye, Peng Li, Xu Han, Yankai Lin, Zhenghao Liu, Zhiyuan Liu, Lixin Huang, Jie Zhou, Maosong Sun
| Challenge: | Existing relation extraction methods focus on extracting intra-sentence relations for single entities. |
| Approach: | They propose a relation extraction dataset from Wikipedia and Wikidata with three features . document-level relation extraction is a task to identify relational facts between entities . |
| Outcome: | The proposed dataset is the largest human-annotated dataset for document-level RE from plain text. |