Papers by Young Jin Kim
PEMA: An Offsite-Tunable Plug-in External Memory Adaptation for Language Models (2024.naacl-long)
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| Challenge: | Pre-trained language models (PLMs) show impressive performance in various downstream NLP tasks. |
| Approach: | They propose a Parameter-Efficient Fine-Tuning method that integrates with context representations from test data to perform downstream tasks. |
| Outcome: | The proposed method outperforms other methods in memory and latency efficiency and maintains sentence meaning and generating appropriate language and styles. |
AutoMoE: Heterogeneous Mixture-of-Experts with Adaptive Computation for Efficient Neural Machine Translation (2023.findings-acl)
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Ganesh Jawahar, Subhabrata Mukherjee, Xiaodong Liu, Young Jin Kim, Muhammad Abdul-Mageed, Laks Lakshmanan, V.S., Ahmed Hassan Awadallah, Sebastien Bubeck, Jianfeng Gao
| Challenge: | Existing MoE designs do not consider computational constraints (e.g., FLOPs, latency) Existing works in MoE consider homogeneous design where the same number of experts of the same size are placed uniformly throughout the network. |
| Approach: | They propose a framework for designing heterogeneous MoEs under computational constraints. |
| Outcome: | The proposed framework achieves 4x inference speedup and FLOPs reduction over manual models and within 1 BLEU point of MoE SwitchTransformer over benchmark datasets for NMT. |
From Research to Production and Back: Ludicrously Fast Neural Machine Translation (D19-56)
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Young Jin Kim, Marcin Junczys-Dowmunt, Hany Hassan, Alham Fikri Aji, Kenneth Heafield, Roman Grundkiewicz, Nikolay Bogoychev
| Challenge: | Using the dominating submissions to the previous edition of the shared task, we develop improved teacher-student training via multi-agent dual-learning and noisy backward-forward translation for Transformer-based student models. |
| Approach: | They propose to use multi-agent dual-learning and noisy backward-forward translation to improve teacher-student training for Transformer-based student models. |
| Outcome: | The proposed model outperforms submissions to the previous edition of the WNGT efficiency shared task by 4 BLEU points and 10 BLUE points respectively. |