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Output scanner

As explained in Transformers in Action

An output scanner is a postprocessing component that examines generated model content for safety, privacy, factuality, or other policy violations.

An output scanner postprocesses generated language-model responses, checking factual consistency, malicious URLs, personally identifiable information, and other sensitive content before delivery. section 10.4

A safeguard pipeline runs input checks, model inference, and output scanning, returning validity results, scores, statuses, and a blocked response when a check fails.

Protecting personal information

A prompt requesting John Doe's personal data can produce a response without guardrails; applying the safeguard function routes the result through output scanning.

How Transformers in Action covers it

How deep it goes
Shows configurable scanners and a staged safeguard function, but does not detail scanner model internals.
Examples
Demonstrates factuality, malicious URLs, sensitive entities, and personal-information requests through configurable Python listings.
What it assumes
Assumes a language-model inference function and introduces scanner configuration within a broader input-output safeguard pipeline.
What you can do after
Build it from scratch using configurable scanners and a function that blocks or returns checked model responses.
How it connects
Connects output scanning with input scanning, model inference, factual consistency, malicious URL detection, and sensitive-entity redaction.
Cover of Transformers in Action
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Related concepts

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