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..
Fast Multi-Resolution Transformer Fine-tuning for Extreme Multi-label Text Classification
Authors: Jiong Zhang, Wei-Cheng Chang, Hsiang-Fu Yu, Inderjit Dhillon
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results show that XR-Transformer takes significantly less training time compared to other transformer-based XMC models while yielding better state-of-the-art results. In particular, on the public Amazon-3M dataset with 3 million labels, XR-Transformer is not only 20x faster than X-Transformer but also improves the Precision@1 from 51% to 54%. |
| Researcher Affiliation | Collaboration | Jiong Zhang Amazon EMAIL Wei-cheng Chang Amazon EMAIL Hsiang-fu Yu Amazon EMAIL Inderjit S. Dhillon UT Austin & Amazon EMAIL |
| Pseudocode | Yes | Algorithm 1: Iterative_Learn(X, Y, C, θ, P) ... Algorithm 2: XR-Transformer training |
| Open Source Code | Yes | Our code is publicly available at https://github.com/amzn/pecos. |
| Open Datasets | Yes | We evaluate XR-Transformer on 6 public XMC benchmarking datasets: Eurlex-4K, Wiki10-31K, Amazon Cat-13K, Wiki-500K, Amazon-670K, Amazon-3M. ... These six publicly available benchmark datasets, including the sparse TF-IDF features are downloaded from https://github.com/yourh/Attention XML which are the same as Attention XML [8] X-Transformer [12] and Light XML [13] for fair comparison. |
| Dataset Splits | Yes | For fair comparison, we use the same raw text input, sparse feature representations and same train-test split as Attention XML [8] and other latest works [12, 13]. |
| Hardware Specification | Yes | all the experiments are conducted with float32 precision on AWS p3.16xlarge instance with 8 Nvidia V100 GPUs |
| Software Dependencies | No | No specific software dependencies with version numbers were explicitly mentioned for reproducibility. |
| Experiment Setup | Yes | The hyper-parameter of XR-Transformer and more empirical results are included in Appendix A.3. The proposed XR-Transformer follows Attention XML and Light XML to use an ensemble of 3 models, while X-Transformer uses an ensemble of 9 models [12]. More details about the ensemble setting can be found in Appendix A.3. |