How deeply does Knowledge Graphs and LLMs in Action cover zero-shot learning?

Defines the technique and gives an iterative validation workflow, but stops short of implementation details or formal analysis.

Zero-shot learning

As explained in Knowledge Graphs and LLMs in Action

Zero-shot learning is performing a task without task-specific examples, relying on a task description and pretrained knowledge.

Zero-shot learning performs a task from a task description alone, without task-specific examples. It relies on a pretrained language model’s broad linguistic understanding and general knowledge. section 5.3.3

A practical zero-shot workflow is:

Describe the extraction task and provide clean, complete relation and entity specifications. section 5.3.3
Run a small iteration, produce a mini knowledge graph, and inspect it for systematic errors. section 5.3.3
Improve the prompt briefly; switch to fine-tuning when prompt changes stop producing satisfactory results. section 5.3.3

Domain extraction

A prompt can ask an LLM to extract relations from unstructured text without examples. The model may resolve “Prof. Chem.” as Professor of Chemistry and “Stanford” as Stanford University from context, but complex reasoning and dataset ambiguities can still cause errors. section 5.3.3

How Knowledge Graphs and LLMs in Action covers it

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Also explained in

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Introduction to Generative AI, Second EditionZero-shot learning is performing a task without task-specific labeled examples or demonstrations.
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Starting Data Analytics with Generative AI and PythonA zero-shot learning model performs a task or makes predictions for categories without task-specific examples for those categories during inference.
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Sutskever's ListA zero-shot setting evaluates a model on a task or query without providing task-specific examples in the input.
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LLMs in ProductionA learning or inference setting in which a model performs a task without being given task-specific examples.
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Generative AI in ActionZero-shot learning is the ability to perform a task or recognize classes not directly represented in task-specific training examples by relying on broader learned associations or descriptions.
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Hugging Face in ActionZero-shot classification is the assignment of labels to inputs without task-specific labeled examples during model adaptation or inference.
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Transformers in ActionZero-shot learning is the ability to perform a task or recognize classes without task-specific examples during adaptation or inference.
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Build a Large Language Model (From Scratch)A learning setup where a model handles a task without having seen any task-specific examples beforehand.
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Reinforcement Learning from Human FeedbackZero-shot evaluation measures a model on questions or tasks without providing task-specific examples in the prompt.

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Related concepts

few-shot learningcontrasts with · Few-shot learning is the adaptation or performance of a model on a task using only a small number of task-specific examples.
prompt engineeringused by · Prompt engineering is the deliberate design and refinement of instructions, context, examples, or other inputs to guide an AI model’s behavior or outputs.
one-shot learningcontrasts with · One-shot learning is a machine-learning setting in which a model generalizes to recognize or perform a task from a single labeled or demonstrated example.
pretraininguses · Pretraining is the initial training of a model on broad data to learn general patterns or capabilities before adaptation to specific tasks or domains.
DetectGPTincludes · DetectGPT is a zero-shot method for detecting machine-generated text by analyzing changes in language-model likelihoods under perturbations.
NLIuses · Natural language inference is the task of determining whether a hypothesis is entailed by, contradicts, or is neutral with respect to a premise.

This explainer was generated by Manning's knowledge graph from the text of Knowledge Graphs and LLMs in Action. The section links point to the parts of the book it draws on.