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

Defines the prompting pattern, gives iteration and validation advice, and stops short of solving complex reasoning ambiguities.

One-shot learning

As explained in Knowledge Graphs and LLMs in Action

One-shot learning is performing a task after receiving one task-specific example.

One-shot learning performs a task after receiving one task-specific example, typically alongside a task description. With a pretrained language model, the example guides domain-specific extraction without additional model training. section 5.3.3

A practical one-shot workflow:

Provide one ambitious example using complete, clean entity names and relations. section 5.3.3
Run quick prompt iterations, then generate and inspect a small knowledge graph for systematic failures. section 5.3.3
Move to fine-tuning when repeated prompt improvements still produce unsatisfactory results. section 5.3.3

Entity-aware extraction

A prompt can demonstrate that “Prof. Chem.” means Professor of Chemistry and that “Stanford” means Stanford University, encouraging cleaner knowledge-graph relations and reducing later entity cleansing. section 5.3.3

How Knowledge Graphs and LLMs in Action covers it

Cover of Knowledge Graphs and LLMs in Action

liveBook manning.com

Read Knowledge Graphs and LLMs in Action on liveBook Get the book at manning.com

Related concepts

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.
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.
machine learningkind of · Machine learning is a field of computational methods in which systems learn patterns or relationships from data to make predictions, decisions, or other outputs.
few-shot learningalternative to · Few-shot learning is the adaptation or performance of a model on a task using only a small number of task-specific examples.
one-shot promptinguses · One-shot prompting is a prompting technique that provides a language model with a single task-related example to guide its response.
shot learningkind of · A prompting approach that provides zero or more examples to guide the output behavior of a language model.

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.