Introducing hallucination correction

The potential of Large Language Models (LLMs) is transforming industries, but a significant hurdle remains: the tendency for these models to hallucinate. This doesn't just undermine user confidence, it introduces real risks for businesses relying on AI outputs for critical functions. At Vectara, we believe building trustworthy and safe AI is paramount to the broader, safe adoption of this technology.

This is why we're excited to be adding hallucination correction to our platform, a new capability designed to directly address this challenge head-on by identifying hallucinations and offering suggested corrections to improve generative quality.

Dual impact: business risk and user trust

When an AI application fabricates answers, the consequences extend far beyond user frustration. For businesses, especially those in regulated or high-stakes fields (e.g., Finance, Healthcare, Legal), relying on unverified AI outputs can lead to significant operational errors, compliance failures, financial liabilities, and severe reputational damage.

These AI hallucinations aren't just minor glitches; they represent potential landmines in critical workflows. Without robust guardrails to identify and address these inaccuracies, the risk of incorporating faulty AI-generated information into reports, decisions, or customer interactions can expose businesses to unnecessary risk and hinder confident AI adoption.

Hallucination correction as a business imperative

Simply detecting potential hallucinations isn't enough to mitigate these substantial risks effectively. Businesses need practical tools to actively correct inaccuracies before they cause harm. That's where Vectara’s Hallucination Corrector (VHC) comes in.

Building upon our established factual consistency checks, VHC analyzes why a statement in an AI summary is considered inaccurate based on the provided source documents. Crucially, it then proposes a corrected version, making only minimal changes for factual alignment. This process provides a vital check, helping ensure AI-generated summaries align with source facts before they propagate into critical business processes or reach end users.

How it works: explanation and correction

VHC takes the AI generated summary and the source text(s) as input. A sophisticated model compares the two, identifying specific statements in the summary unsupported by the source material.

The key output provides clear, structured feedback:

• Corrections: An explanation of what was identified as inaccurate and why
• Corrected Summary: A revised summary with the inaccuracies fixed

Here’s a conceptual example of what the API interaction might look like:

Example request

Example response

A trusted platform for AI

VHC is a key component of Vectara's broader commitment to leading the way in hallucination detection and correction. Our vision is to provide a platform where businesses can build and deploy AI applications with confidence, knowing they have robust tools to ensure factual accuracy and operational reliability.

Releasing VHC addresses a critical need for safer AI deployment. Alongside the VHC feature, we are also releasing an open source Hallucination Correction Benchmark. This separate toolkit provides a standardized way to measure correction model performance across the industry.

This dual commitment to integrated platform tools and open standards reflects our dedication to being a trusted platform for AI development, helping the entire ecosystem build safer, more dependable systems.

Conclusion

For the latest documentation about VHC and how to use it, have a look here.

As always, we’d love to hear your feedback! Connect with us on our forums or on our Discord. If you’d like to see what Vectara can offer you for retrieval augmented generation on your application or website, sign up for an account!

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