MiniALBERT: Model Distillation via Parameter-Efficient Recursive Transformers (2023.eacl-main)
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| Challenge: | Pre-trained Language Models (LMs) are an integral part of natural language processing but their usability is constrained by computational and time complexity and their increasing size. |
| Approach: | They propose a technique for converting knowledge of fully parameterised LMs into a compact recursive student. |
| Outcome: | The proposed models match the performance of bloated models with negligible performance losses. |
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Cheng Chen, Yichun Yin, Lifeng Shang, Xin Jiang, Yujia Qin, Fengyu Wang, Zhi Wang, Xiao Chen, Zhiyuan Liu, Qun Liu
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| Challenge: | Large pre-trained models suffer from domain shift and are not optimal for specific domains. |
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| Challenge: | Existing methods for training pre-trained language models have limited practicality due to latency requirements. |
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MergeDistill: Merging Language Models using Pre-trained Distillation (2021.findings-acl)
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| Challenge: | Existing pre-trained multilingual language models often lack capacity and skewed data . this leads to inequitable representation of languages due to limited capacity and sub-optimal vocabularies. |
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FastBERT: a Self-distilling BERT with Adaptive Inference Time (2020.acl-main)
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| Challenge: | Pre-trained language models like BERT have proven to be highly performant, but are often computationally expensive in many practical scenarios. |
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