Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
Chain-of-thought prompting explores whether intermediate reasoning examples improve language-model performance on multi-step tasks.
Paper & contextEliciting intermediate reasoning from language models.
Wei studies how language models reason and how their capabilities can be measured. This selection connects chain-of-thought prompting with evaluation of browsing, factuality, and complex tasks that require more than a short answer.
10 papers
Chain-of-thought prompting explores whether intermediate reasoning examples improve language-model performance on multi-step tasks.
Paper & contextThe GPT-4 technical report documents capabilities, evaluations and limitations of a multimodal model, while withholding many architecture and training details.
Paper & contextSelected research in reasoning. Read the full paper, including the methods, experiments, and reported results.
Paper & contextSelected research in reasoning. Read the full paper, including the methods, experiments, and reported results.
Paper & contextBIG-bench collects a broad set of language-model evaluation tasks to study capabilities and limitations beyond a single benchmark.
Paper & contextSelected research in reasoning. Read the full paper, including the methods, experiments, and reported results.
Paper & contextSelected research in machine learning, ai evaluation. Read the full paper, including the methods, experiments, and reported results.
Paper & contextSelected research in language models, reasoning. Read the full paper, including the methods, experiments, and reported results.
Paper & contextSelected research in reasoning. Read the full paper, including the methods, experiments, and reported results.
Paper & contextSelected research in reinforcement learning, reasoning. Read the full paper, including the methods, experiments, and reported results.
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