Papers by Xiyuan Zhou
Proactive Human-Machine Conversation with Explicit Conversation Goal (P19-1)
Copied to clipboard
| Challenge: | Typical human-machine conversation systems only use utterances and responses as training data, which results in uninformative and inappropriate responses. |
| Approach: | They propose a dataset where one acts as a conversation leader and the other as 'follower' they establish baseline results on a 270K utterances and 30k dialogues dataset using state-of-the-art models. |
| Outcome: | The proposed model can generate diverse multi-turn conversations using knowledge from a new dataset . |
D-NET: A Pre-Training and Fine-Tuning Framework for Improving the Generalization of Machine Reading Comprehension (D19-58)
Copied to clipboard
Hongyu Li, Xiyuan Zhang, Yibing Liu, Yiming Zhang, Quan Wang, Xiangyang Zhou, Jing Liu, Hua Wu, Haifeng Wang
| Challenge: | MRC requires machines to understand text and answer questions about the text. |
| Approach: | They propose a simple system Baidu submitted for MRQA 2019 Shared Task that focused on generalization of machine reading comprehension (MRC) models. |
| Outcome: | The proposed system is ranked at top 1 of all participants in terms of averaged F1 score. |
EngiBench: A Benchmark for Evaluating Large Language Models on Engineering Problem Solving (2026.findings-acl)
Copied to clipboard
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. |