Papers by Xiyuan Zou
A Controlled Reevaluation of Coreference Resolution Models (2024.lrec-main)
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| Challenge: | a pretrained language model is used in state-of-the-art coreference resolution models. |
| Approach: | They evaluate five coreference resolution models and control for language model used . they find that encoder-based CR models outperform decoder--based models in accuracy . |
| Outcome: | The encoder-based model outperforms the decoder--based models in accuracy and speed . older model generalizes the best to out-of-domain textual genres . |
CItruS: Chunked Instruction-aware State Eviction for Long Sequence Modeling (2024.emnlp-main)
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| Challenge: | Recent studies have found that information relevant to the next token prediction task accumulates in the hidden representations of just a few tokens. |
| Approach: | They propose a method that integrates attention preferences useful for a downstream task into the eviction process of hidden states. |
| Outcome: | The proposed method performs better on comprehension and retrieval tasks while preserving language modeling perplexity. |
Separately Parameterizing Singleton Detection Improves End-to-end Neural Coreference Resolution (2024.naacl-short)
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| Challenge: | Current end-to-end coreference resolution models combine detection of singleton mentions and antecedent linking into a single step. |
| Approach: | They add a singleton detector to a coarse-to-fine coreference model and design an anaphoricity-aware span embedding and singletont detection loss. |
| Outcome: | The proposed method significantly improves model performance on OntoNotes and four additional datasets. |
EngiBench: A Benchmark for Evaluating Large Language Models on Engineering Problem Solving (2026.findings-acl)
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Xiyuan Zhou, Xinlei Wang, Yirui He, Ruixi Zou, Yang Wu, Yuheng Cheng, Yulu Xie, Wenxuan Liu, Huan Zhao, Yan Xu, Jinjin Gu, Junhua Zhao
| Challenge: | Existing benchmarks focus on well-defined or abstract reasoning and fail to capture real-world engineering problems. |
| Approach: | They propose a hierarchical benchmark to evaluate large language models on engineering problems. |
| Outcome: | The proposed model performs well under well-defined conditions and is based on three levels of difficulty and covers diverse engineering subfields. |