no code implementations • 20 Mar 2025 • Langming Liu, Haibin Chen, Yuhao Wang, Yujin Yuan, Shilei Liu, Wenbo Su, Xiangyu Zhao, Bo Zheng
To bridge the evaluation gap, we propose ECKGBench, a dataset specifically designed to evaluate the capacities of LLMs in e-commerce knowledge.
1 code implementation • 27 Feb 2025 • Haibin Chen, Kangtao Lv, Chengwei Hu, Yanshi Li, Yujin Yuan, Yancheng He, Xingyao Zhang, Langming Liu, Shilei Liu, Wenbo Su, Bo Zheng
To address these problems, we propose \textbf{ChineseEcomQA}, a scalable question-answering benchmark focused on fundamental e-commerce concepts.
no code implementations • Findings (ACL) 2022 • Junhao Zheng, Haibin Chen, Qianli Ma
Cross-domain NER is a practical yet challenging problem since the data scarcity in the real-world scenario.
1 code implementation • 22 Feb 2024 • Yanan Wu, Jie Liu, Xingyuan Bu, Jiaheng Liu, Zhanhui Zhou, Yuanxing Zhang, Chenchen Zhang, Zhiqi Bai, Haibin Chen, Tiezheng Ge, Wanli Ouyang, Wenbo Su, Bo Zheng
This paper introduces ConceptMath, a bilingual (English and Chinese), fine-grained benchmark that evaluates concept-wise mathematical reasoning of Large Language Models (LLMs).
2 code implementations • 19 Jun 2023 • Junhao Zheng, Qianli Ma, Shengjie Qiu, Yue Wu, Peitian Ma, Junlong Liu, Huawen Feng, Xichen Shang, Haibin Chen
Intriguingly, the unified objective can be seen as the sum of the vanilla fine-tuning objective, which learns new knowledge from target data, and the causal objective, which preserves old knowledge from PLMs.
1 code implementation • 8 Oct 2022 • Junhao Zheng, Zhanxian Liang, Haibin Chen, Qianli Ma
Thanks to the causal inference, we identify that the forgetting is caused by the missing causal effect from the old data.
Ranked #1 on
FG-1-PG-1
on conll2003
1 code implementation • ACL 2021 • Haibin Chen, Qianli Ma, Zhenxi Lin, Jiangyue Yan
We then introduce a joint embedding loss and a matching learning loss to model the matching relationship between the text semantics and the label semantics.
1 code implementation • Springer 2020 • Yuchong Gu, Zitao Zen, Haibin Chen, Jun Wei, Yaqin Zhang, Binghui Chen, Yingqin Li, Yujuan Qin, Qing Xie, Zhuoren Jiang, Yao Lu
Super-resolution (SR) in medical imaging is an emerging application in medical imaging due to the needs of high quality images acquired with limited radiation dose, such as low dose Computer Tomography (CT), low field magnetic resonance imaging (MRI).