liveBook · Manning

Hallucination

As explained in Machine Learning System Design

A generative-model behavior in which false or unsupported information is presented as if it were true.

Hallucination is a generative-model behavior in which false information is presented as true. Large language models can answer questions zero-shot, but their knowledge remains implicit and difficult to modify directly. section 3.3

Practical responses focus on giving model outputs access to maintainable external information:

  • Use online sources through plugins, similar to search-engine use. section 3.3
  • Generate executable code or call third-party APIs when precise, externally grounded answers are needed. section 3.3
  • Treat these approaches as promising but not yet widely established in production systems. section 3.3

How Machine Learning System Design covers it

How deep it goes
Defines false presentation of information and surveys grounding directions, but stops short of detection or mitigation procedures.
Examples
Contrasts GPT-4's zero-shot answers with Bing AI, ChatGPT plugins, Galactica, Toolformer, and LangChain.
What you can do after
Evaluate why zero-shot language-model answers may need external sources, executable tools, or API access.
How it connects
Connects hallucination with large language models, zero-shot learning, external knowledge sources, plugins, and third-party APIs.

Also explained in

Compare how 29 books explain hallucination →

Related concepts

This explainer was generated by Manning's knowledge graph from the text of Machine Learning System Design. The section links point to the parts of the book it draws on.