Notice: 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. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Enhancing Multi-Label Classification via Dynamic Label-Order Learning
Authors: Jiangnan Li, Yice Zhang, Shiwei Chen, Ruifeng Xu
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on public datasets reveal that our approach greatly outperforms previous methods. |
| Researcher Affiliation | Academia | 1Harbin Institute of Technology, Shenzhen, China 2Peng Cheng Laboratory, Shenzhen, China 3Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies |
| Pseudocode | Yes | Algorithm 1: The proposed label-order learning algorithm |
| Open Source Code | Yes | We will release our code at https: //github.com/Kagami Baka/DLOL. |
| Open Datasets | Yes | The dataset used is Reuters-21578 (Hayes and Weinstein 1990). and we evaluate our approach on four typical multilabel classification datasets, namely Reuters-21578, RCV1-V2, Slashdot, and Go Emotions. |
| Dataset Splits | Yes | Table 3: Statistics of four datasets. (includes columns for Train, Dev, Test sample counts) |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions using BART-base as the backbone model but does not specify software dependencies with version numbers (e.g., Python, PyTorch, or CUDA versions). |
| Experiment Setup | Yes | Our approach has three hyper-parameters for multi-reference training, label smoothing, and eos penalty... the optimal values for these hyper-parameters are α = 0.1, β = 0.1, and γ = 0.9, respectively. |