Research/Academic • NLP/LLM
About The Project
Built a modular MoE pipeline where a TaskClassifier directs inputs to expert models—BART-large-cnn for summarization and BERT-base for sentiment—achieving efficient, scalable performance without retraining the entire stack. Designed with hooks for future multi-modal experts (vision/audio).
Achievements
98.95% task-routing accuracy
~30% ROUGE-1 improvement on summarization (BART-large-cnn)
92.1% accuracy on SST-2 sentiment (BERT-base)
Extensible design for multi-modal (image) experts
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Links
sunny.sunho.park@gmail.com