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Guardrails for LLMs: Measuring AI ‘Hallucination’ and Verbosity

This article discusses how to implement an infrastructure for measuring and controlling overly verbose LLM responses.

SourceKDnuggetsAuthor: Iván Palomares Carrascosa
Guardrails for LLMs: Measuring AI ‘Hallucination’ and Verbosity
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Summary only

Under the source content permission, this page provides an editorial summary, key points, and a link to the original article.

Key points and analysis

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Key points

  • Verbose LLM responses often correlate with increased hallucination risks.
  • The Textstat library's Automated Readability Index (ARI) quantifies text complexity.
  • A re-prompting loop enforces a complexity budget by requesting simplifications.
  • The guardrail is implemented in a LangChain pipeline using distilgpt2.

Highlights and analysis are generated automatically and may contain errors. Check the original source.

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