How deeply does Sutskever's List cover few-shot learning?

Connects few-shot learning to reasoning scaffolds but does not detail how to construct or select examples.

Few-shot learning

A learning or prompting approach in which a model performs a task from a small number of examples supplied during evaluation or adaptation.

Few-shot learning uses a small number of examples to help a model perform a task. It offers a way to guide behavior without task-specific fine-tuning: GPT-3 demonstrated in-context learning and competence on tasks without that additional training. section 6.0

For reasoning, few-shot learning can act as a scaffold alongside chain-of-thought prompting and test-time compute. These supports improve performance, but reasoning remains brittle when the rules change. section 7.5

How Sutskever's List covers it

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Software Testing with Generative AIFew-shot prompting is a prompting technique that supplies a small number of explicit examples to clarify instructions and guide a generative model's output.
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Natural Language Processing in Action, Second EditionFew-shot learning is adapting a model to perform a task from a small number of examples supplied at inference time, often in the prompt.
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AI Agents and ApplicationsPrompt examples are illustrative instances supplied in a prompt to guide a language model toward a desired task behavior or output pattern.
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Effective Conversational AIFew-shot prompting is the practice of providing a language model with a small number of example demonstrations in a prompt to guide its response.
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LLMs in ProductionFew-shot learning is the adaptation or prediction of a model from only a small number of labeled examples.
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Introduction to Generative AI, Second EditionFew-shot prompting is a technique that guides a model by providing a small number of task-specific examples in the input.
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Build AI-Enhanced Web AppsFew-shot learning is a technique in which a model performs or adapts to a task by inferring the desired behavior from a small number of examples.
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Generative AI in ActionFew-shot learning is the adaptation of a model to a task by providing a small number of task-specific examples at use time.
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The Art of AI Product DevelopmentFew-shot prompting is prompting a model with a small number of input-output examples to guide its behavior on a new input.
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Data Analysis with LLMsFew-shot learning is an approach in which a model infers a task from a small number of examples specifying inputs and desired outputs.
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Time Series Forecasting Using Foundation ModelsA few-shot prompt is a model input that includes a small number of examples to demonstrate the desired task or output behavior.
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The Complete Obsolete Guide to Generative AIFew-shot learning is adapting a model to a task by conditioning it on a small number of task-specific examples or demonstrations.
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Transformers in ActionFew-shot learning is the adaptation of a model to a new task using only a small number of task-specific examples.
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Learn Generative AI with PyTorchA prompting approach that provides exactly one example to condition a large language model on the task.
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AI for Everyday ITFew-shot prompting is a technique that supplies an AI model with a small number of examples in the prompt to guide its behavior on a task.
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Build a Large Language Model (From Scratch)The ability to learn a new task from only a few examples rather than extensive training data.
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Deep Learning with R, Third EditionFew-shot learning is adapting or directing a model to perform a task from only a small number of examples.

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Related concepts

in-context learningkind of · In-context learning is a model’s adaptation of its behavior to examples, instructions, or other information in its input context without updating its parameters.
prompt engineeringkind of · Prompt engineering is the deliberate design and refinement of instructions, context, examples, or other inputs to guide an AI model’s behavior or outputs.
language modeluses · A language model is a computational model that represents patterns in linguistic tokens to estimate sequence probabilities and predict or generate language from context.
promptuses · An input, typically containing instructions or context, supplied to an artificial-intelligence model to guide or elicit its output.
zero-shot promptingcontrasts with · Zero-shot prompting asks a model to perform a task through instructions or a task description without providing task-specific examples or demonstrations.
zero-shot learningcontrasts with · Zero-shot learning is a setting in which a model performs a task or predicts categories without task-specific examples during training, adaptation, or inference, relying on learned knowledge and available task information.

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