공대생 도전 일지

https://arxiv.org/abs/2005.14165 Language Models are Few-Shot LearnersRecent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific task. While typically task-agnostic in architecture, this method still requires task-specific fiarxiv.orghttps://arxiv.org/abs/2201.11903 Chain-of-Thought Prompting Eli..

https://arxiv.org/abs/2201.11903 Chain-of-Thought Prompting Elicits Reasoning in Large Language ModelsWe explore how generating a chain of thought -- a series of intermediate reasoning steps -- significantly improves the ability of large language models to perform complex reasoning. In particular, we show how such reasoning abilities emerge naturally in suarxiv.org 직전에 봤던 논문의 연장선 같은 느낌입니다.Few-Sh..

https://arxiv.org/abs/2005.14165 Language Models are Few-Shot LearnersRecent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific task. While typically task-agnostic in architecture, this method still requires task-specific fiarxiv.orgFew-Shot은 이 그림으로 명확하게 설명이 가능하겠네요 파라미터의 변경 없이 Prompt에 몇 개의 예시만으로..