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..
SKDBERT: Compressing BERT via Stochastic Knowledge Distillation
Authors: Zixiang Ding, Guoqing Jiang, Shuai Zhang, Lin Guo, Wei Lin
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on GLUE benchmark show that SKDBERT reduces the size of a BERT model by 40% while retaining 99.5% performances of language understanding and being 100% faster. |
| Researcher Affiliation | Industry | Zixiang Ding *1, Guoqing Jiang1, Shuai Zhang1, Lin Guo1, Wei Lin2 1Meituan 2Individual EMAIL, EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described in this paper. While it references the supplementary materials PDF and third-party code, it does not provide a link to the authors' own implementation code. |
| Open Datasets | Yes | We evaluate SKDBERT on the GLUE benchmark including MRPC (Dolan and Brockett 2005), RTE (Bentivogli et al. 2009), STS-B (Cer et al. 2017), SST-2 (Socher et al. 2013), QQP (Chen et al. 2018), QNLI (Rajpurkar et al. 2016) and MNLI (Williams, Nangia, and Bowman 2017). |
| Dataset Splits | Yes | Table 1: Distillation performances of our student with single and multiple teachers on the development set of GLUE benchmark (Wang et al. 2019). |
| Hardware Specification | Yes | Moreover, all implementations are performed on NVIDIA A100 GPU. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with version numbers). |
| Experiment Setup | No | The paper mentions that hyperparameters are shown in Section E of supplementary materials, but does not provide specific experimental setup details such as concrete hyperparameter values or training configurations in the main text. |