Hyung Won Chung
Research Scientist, OpenAI | Co-Creator of Flan-T5 & Instruction-Tuning Pioneer
Verified Proof of Work Artifacts
2 items catalogedEach artifact below represents an authenticated research publication, production code repository, or technical architectural framework directly authored or co-created by Hyung Won Chung. Every entry undergoes editorial source verification.
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
Co-authored the foundational paper discovering that generating step-by-step reasoning tokens before the final answer dramatically unlocks multi-step arithmetic, commonsense, and symbolic reasoning in foundation models.
Chain-of-thought benefits appear as an emergent property only in models with sufficient scale (~100B+ parameters or instruction-tuned compact models).
Scaling Instruction-Finetuned Language Models (Flan-T5 & Flan-PaLM)
Spearheaded the research demonstrating that instruction fine-tuning scaled across 1,836 diverse tasks and 540 billion parameters enhances task-general reasoning, chain-of-thought problem solving, and zero-shot performance across every benchmark.
Instruction tuning without careful task balance can degrade pure memorization capacity on open-domain fact retrieval.