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..
AdaBERT: Task-Adaptive BERT Compression with Differentiable Neural Architecture Search
Authors: Daoyuan Chen, Yaliang Li, Minghui Qiu, Zhen Wang, Bofang Li, Bolin Ding, Hongbo Deng, Jun Huang, Wei Lin, Jingren Zhou
IJCAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate Ada BERT on several NLP tasks, and the results demonstrate that those task-adaptive compressed models are 12.7x to 29.3x faster than BERT in inference time and 11.5x to 17.0x smaller in terms of parameter size, while comparable performance is maintained. |
| Researcher Affiliation | Industry | Daoyuan Chen , Yaliang Li , Minghui Qiu, Zhen Wang, Bofang Li, Bolin Ding, Hongbo Deng, Jun Huang, Wei Lin, Jingren Zhou Alibaba Group |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a link to a code repository for their methodology. |
| Open Datasets | Yes | We evaluate the proposed Ada BERT method on six datasets from GLUE [Wang et al., 2019a] benchmark. |
| Dataset Splits | Yes | We evaluate the proposed Ada BERT method on six datasets from GLUE [Wang et al., 2019a] benchmark. |
| Hardware Specification | No | The paper mentions that "The inference time is tested with a batch size of 128 over 50, 000 samples." but does not specify any hardware details like GPU/CPU models or memory. |
| Software Dependencies | No | The paper mentions optimizers used (SGD, Adam) and their parameters, but does not specify software dependencies with version numbers (e.g., Python 3.x, TensorFlow 2.x, PyTorch 1.x). |
| Experiment Setup | Yes | For Ada BERT, we set Ī³ = 0.8, Ī² = 4, T = 1, inner node N = 3 and search layer Kmax = 8. We search PĪ± for 80 epochs and derive the searched structure with its trained operation weights. |