Language Models are Few-Shot Learners

  • 类型：arxiv
  • 标识：2005.14165
  • 链接：https://arxiv.org/abs/2005.14165
  • 主题：evaluation
  • 主分类：llm-infra
  • 形态：method
  • 被引：62417
  • 被引来源：Semantic Scholar
  • S2被引：62417
  • OpenAlex被引：3020
  • 影响力被引：5433
  • TLDR：GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic.
  • OpenAlex ID：W3030163527
  • OpenAlex DOI：10.48550/arxiv.2005.14165
  • DOI：10.48550/arxiv.2005.14165
  • DOI来源：OpenAlex
  • 开放获取：green
  • 开放获取链接：https://arxiv.org/pdf/2005.14165
  • OpenAlex更新：2026-08-25
  • 待LLM分类：否
  • 成熟度：production
  • 场景：few-shot learning、language model、scaling
  • 标题中文：Language Models are Few-Shot Learners
  • TLDR中文：GPT-3 在多个 NLP 数据集上取得了强劲表现，包括翻译、问答和完形填空任务，以及若干需要即时推理或领域适应的任务，例如乱序词重组、在句子中使用新词、或执行三位数算术运算。
  • 来源文件：
  • [OpenAlex discover]
  • [S2 enrich]