Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates.
Curriculum-Meta Learning for Order-Robust Continual Relation Extraction
Authors: Tongtong Wu, Xuekai Li, Yuan-Fang Li, Gholamreza Haffari, Guilin Qi, Yujin Zhu, Guoqiang Xu10363-10369
AAAI 2021 | Venue PDF | LLM Run Details | Input Tokens: 15,975 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,915 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our comprehensive experiments on three benchmark datasets show that our proposed method outperforms the state-of-the-art techniques. |
| Researcher Affiliation | Collaboration | 1School of Computer Science and Engineering, Southeast University, Nanjing, China 2Faculty of Information Technology, Monash University, Melbourne, Australia 3Gamma Lab, Ping An One Connect, Shanghai, China |
| Pseudocode | Yes | Algorithm 1: Curriculum-Meta Learning |
| Open Source Code | Yes | The code is available at https://github.com/wutong8023/AAAI-CML. |
| Open Datasets | Yes | We conduct our experiments on three datasets, including Continual-Few Rel, Continual-Simple Questions, and Continual-TACRED, which were introduced in (?). |
| Dataset Splits | No | The paper mentions forming training and testing sets but does not specify a separate validation split or its details. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory, or cloud instance types) used for running experiments are mentioned in the paper. |
| Software Dependencies | No | The paper mentions the Adam optimizer but does not provide specific version numbers for any software dependencies or libraries. |
| Experiment Setup | No | The paper states 'see Appendix B for hyperparameters', indicating that specific experimental setup details are not provided in the main text. |