Hallucination Detection

Use Case 1: Detect Hallucinations and Data Conflicts in Generative AI Outputs

While many organizations are using of Generative Al, concerns about false information from hallucinations limits their usefulness.

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  Many leading organizations are integrating transformative new technologies such as Large Language Models. Yet today's Large Language Models (LLMs) generate hallucinations - the tendency of the models to generate inaccurate responses. According to recent research, current Generative Al solutions (like ChatGPT) have a hallucination rate of 5% to 20% depending on query type. Agolo functions as a "fact checker" and can identify ~90% of these potential factual errors in generative output.

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  Agolo uses domain-specific information from within your organization's content to build an entity graph representing the ground truth within that domain, then uses that "ground truth" to evaluate generative Al results from large language models. Importantly, Agolo enables the analysts/data scientist to drill down into the underlying document and make changes/corrections as needed in the Agolo Entity Graph. This "human-in-the-loop" capability enables the further reduction of hallucinations over time.

Use Case 2: Achieve Factual, Reliable A.I. Responses

Al hallucinations and misinformation can erode user trust - ensure that every Al response is rooted in a trusted source with Agolo's knowledge base.

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  Generative Al can provide answers that aren't entirely accurate. But with the world demanding factual, transparent, and deterministic Al responses, there's no room for misinformation.

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  By integrating Agolo's Entity Graph with LLMs, and using lower model temperatures, Al systems can ensure that the information being served is from a reliable source of truth. Every Al-generated response is not just accurate but also grounded in trusted data controlled and moderated by you, eliminating hallucinations and cementing user trust.