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Version: 2.0

Hallucination Corrector

Hallucinations remain one of the most pressing challenges in deploying Large Language Models (LLMs) in real-world applications. These inaccuracies can erode user trust and expose organizations to serious business risks.

The Vectara Hallucination Corrector addresses this problem directly by not only detecting unsupported claims in AI-generated summaries, but also offering corrected versions that align with the factual source material. This capability is critical in building safe, reliable AI applications across regulated and high-stakes industries like finance, legal, and healthcare.

Why correcting hallucinations matters​

The impact of AI hallucinations extends far beyond user frustration:

  • Regulatory and compliance risks: In industries with strict reporting or documentation requirements, hallucinated AI output can trigger regulatory violations.
  • Reputational damage: Misinformation in public- or customer-facing content can reduce trust in your brand.
  • Decision-making errors: Inaccurate summaries can lead teams to make the wrong calls, especially when used in analytics or reporting contexts.

The Vectara Hallucination Corrector helps reduce these risks by acting as a factual safeguard before content is delivered or acted upon.

What the Vectara Hallucination Corrector does​

The Vectara Hallucination Corrector provides:

  • Evaluation: Identifies which parts of an AI-generated summary are not grounded in the source content.
  • Explanation: Describes why each inaccurate statement is unsupported.
  • Correction: Returns a revised version of the summary, applying only the minimal changes needed to restore factual alignment.

This ensures that summaries passed through the Vectara Hallucination Corrector retain their intended meaning while reducing or eliminating hallucinations.

How hallucination correction works​

  1. Input: You provide the AI-generated summary and one or more source documents.
  2. Analysis: The Vectara Hallucination Corrector compares the summary to the documents to identify any unsupported or incorrect claims.
  3. Output: The system returns the summary and corrections:
    • corrected_summary: A minimally modified, factually accurate version of the original
    • corrections: A description of what was changed and why

A trusted platform for AI​

Hallucination correction is part of Vectara’s broader observability and quality framework for LLM-powered applications. It complements other platform tools like factual consistency scoring (HHEM) and metadata-based context filtering. When used together, these capabilities offer:

  • Higher-quality summaries and answers
  • Trustworthy outputs for downstream tasks
  • Greater transparency and governance for AI-generated content

Vectara is also releasing an open source Hallucination Correction Benchmark—a toolkit that provides standardized, model-agnostic ways to evaluate correction performance.