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We have not reviewed Language models are few-shot learners yet
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From the listing
About Language models are few-shot learners
This 2020 paper introduced in-context learning, showing that large language models can perform tasks from a few examples without gradient updates. It demonstrated that scaling model size and number of examples improves few-shot performance across a range of NLP benchmarks.
Best for
Researchers and developers exploring few-shot learning with large language models
Use cases
- Classifying text with a handful of labeled examples
- Generating answers or completions from a prompt with demonstrations
- Evaluating model capabilities on new tasks without fine-tuning
Pros
- Established a foundational method for few-shot NLP tasks
- Reduced need for task-specific training data
- Influenced subsequent prompting and in-context learning research
Cons
- Performance is sensitive to prompt wording and example selection
- Requires large models to be effective, limiting accessibility
- Does not provide a mechanism for learning beyond the context window
Key facts
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Sources
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Changelog
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Pairs with
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Prompt Engineering Guide
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🐙 Guides, papers, lessons, notebooks and resources for prompt engineering, context engineering, RAG, and AI Agents.
llm-course
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Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.
LLMsPracticalGuide
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A curated list of practical guide resources of LLMs (LLMs Tree, Examples, Papers)
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