Towards a Holistic Understanding of Mathematical Questions with Contrastive Pre-training
Authors: Yuting Ning, Zhenya Huang, Xin Lin, Enhong Chen, Shiwei Tong, Zheng Gong, Shijin Wang
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments on two real-world mathematical datasets. The experimental results demonstrate the effectiveness of our model. |
| Researcher Affiliation | Collaboration | 1 Anhui Province Key Laboratory of Big Data Analysis and Application, School of Computer Science and Technology, University of Science and Technology of China 2 State Key Laboratory of Cognitive Intelligence 3 i FLYTEK AI Research (Central China), i FLYTEK Co., Ltd. |
| Pseudocode | No | The paper describes the proposed framework and its components in detail but does not include any pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github.com/bigdata-ustc/QuesCo. |
| Open Datasets | No | The paper mentions using two real-world datasets, SYSTEM1 and SYSTEM2, and describes their statistics. However, it does not provide any specific links, DOIs, or citations to publicly access these datasets. |
| Dataset Splits | No | The paper states: "For downstream tasks, we randomly partition labeled questions into training/test sets with 80%/20%." It explicitly mentions train and test sets but does not specify a separate validation split or how validation was handled for hyperparameter tuning. |
| Hardware Specification | Yes | All experiments are conducted with one Tesla V100 GPU. |
| Software Dependencies | No | The paper mentions that the model is "implemented by Py Torch" and uses "BERT-Base-Chinese" as the base encoder, but it does not specify any version numbers for PyTorch or other software dependencies. |
| Experiment Setup | Yes | The projector outputs vectors of size 128. The momentum encoder is updated with a momentum term m = 0.999. The size of memory bank is set to 1,600. Each augmentation strategy is applied with the probability of p = 0.3. We adopt the Adam W optimizer with a learning rate of 0.000005. The temperature factors τi of different ranks in RINCE loss are set to {0.1, 0.1, 0.225, 0.35, 0.6} respectively. |