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..
Movement Pruning: Adaptive Sparsity by Fine-Tuning
Authors: Victor Sanh, Thomas Wolf, Alexander Rush
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that when pruning large pretrained language models, movement pruning shows significant improvements in high-sparsity regimes. |
| Researcher Affiliation | Collaboration | 1Hugging Face, 2Cornell University EMAIL ; EMAIL |
| Pseudocode | No | The paper describes its methods through textual descriptions and mathematical equations, but it does not include a structured pseudocode block or an algorithm labeled as such. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing the source code for the methodology described, nor does it provide a link to a code repository. |
| Open Datasets | Yes | We perform experiments on three monolingual (English) tasks...: question answering (SQu AD v1.1) [Rajpurkar et al., 2016], natural language inference (MNLI) [Williams et al., 2018], and sentence similarity (QQP) [Iyer et al., 2017]. |
| Dataset Splits | No | The datasets respectively contain 8K, 393K, and 364K training examples.' and mentions 'Dev acc/MM acc' in tables, but does not provide explicit training/validation/test splits (e.g., percentages or sample counts for each split) or specific citations for their exact predefined splits that allow reproduction of data partitioning. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU models, CPU types, or server configurations) used to conduct the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies or their version numbers (e.g., specific libraries, frameworks, or programming language versions) used for the experiments. |
| Experiment Setup | Yes | For a given task, we fine-tune the pre-trained model for the same number of updates (between 6 and 10 epochs) across pruning methods. We follow Zhu and Gupta [2018] and use a cubic sparsity scheduling for Magnitude Pruning (Ma P), Movement Pruning (Mv P), and Soft Movement Pruning (SMv P). |