Search Results for author: Haibin Chen

Found 6 papers, 5 papers with code

ConceptMath: A Bilingual Concept-wise Benchmark for Measuring Mathematical Reasoning of Large Language Models

1 code implementation22 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).

Math Mathematical Reasoning

Preserving Commonsense Knowledge from Pre-trained Language Models via Causal Inference

1 code implementation19 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.

Attribute Causal Inference

Distilling Causal Effect from Miscellaneous Other-Class for Continual Named Entity Recognition

1 code implementation8 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.

Causal Inference FG-1-PG-1 +4

Hierarchy-aware Label Semantics Matching Network for Hierarchical Text Classification

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.

text-classification Text Classification

MedSRGAN: medical images super-resolution using generative adversarial networks

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).

Super-Resolution

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