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Harmfulness & Truthfulness Analysis

In healthcare, the generative AI offers promising possibilities to make human life better. Yet it’s crucial to make sure that humans interact with truthful systems that wouldn’t misguide people and that would avoid directing people to harmful situations. That is where Validaitor comes into play and enables a comprehensive evaluation of the LLM based systems.

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The Problem

An LLM developer wants to put in place a new chatbot that would help the patients with their questions. However, the LLM developer is not confident about whether the chatbot would misguide the people by suggesting harmful or hallucinated content.

The Challenge

The hallucinations and harmful contents are among the most pressing concerns among the LLM community. Given that the LLM-based systems can be quite complex, understanding their truthfulness and harmful behavior is a real challenge. Evaluating these systems and understanding their characteristics on edge cases require a good understanding of LLM red and blue teaming.

The Solution

VALIDAITOR provides comprehensive tests to evaluate the LLM based applications for truthfulness and toxicity. Our platform contains public benchmark as well as custom made datasets that are used to test these systems. By enabling the LLM developers and auditors to extensively test LLM-based applications, Validaitor helps its customers in healthcare to put in place robust and safe generative AI solutions.

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