Dr. Jason Wei
AI Research Scientist
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 Dr. Jason Wei. Every entry undergoes editorial source verification.
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
Authored the seminal NeurIPS 2022 research paper demonstrating that generating intermediate reasoning steps dramatically boosts LLM performance on arithmetic, symbolic, and multi-hop reasoning tasks.
Chain-of-thought gains exhibit emergence only at significant model scale (~50B+ parameters); smaller models can generate superficially coherent rationales that lead to incorrect arithmetic conclusions.
Emergent Abilities of Large Language Models
Published the influential study demonstrating that certain capabilities (multi-step arithmetic, translation, symbolic reasoning) are not present in smaller models and emerge non-linearly only once compute and parameter scales cross critical thresholds.
Metric non-linearities (such as exact-string match versus continuous token log-likelihood) can exaggerate the appearance of abrupt phase transitions.