Artificial intelligence agent and assistant platform provider Vectara Inc. today announced the launch of a new Hallucination Corrector directly integrated into its service, designed to detect and mitigate costly, unreliable responses from enterprise AI models.

Hallucinations, when generative AI large language models confidently provide false information, have long plagued the industry. For traditional models, it’s estimated that they occur at a rate of about 3% to 10% of queries on average, depending on the model.

The recent advent of reasoning AI models, which break down complex questions into step-by-step solutions to “think” through them, has led to a noted increase in the hallucination rate.

According to a report from Vectara, DeepSeek-R1, a reasoning model, hallucinates significantly more at 14.3% than its predecessor DeepSeek R3 at 3.9%. Similarly, OpenAI’s GPT-o1, also a reasoning model, jumped to a 2.4% rate from GPT-4o at 1.5%. New Scientist published a similar report and found even higher rates with the same and other reasoning models.

“While LLMs have recently made significant progress in addressing the issue of hallucinations, they still fall distressingly short of the standards for accuracy that are required in highly regulated industries like financial services, healthcare, law and many others,” said Vectara founder and Chief Executive Amr Awadallah.

In its initial testing, Vectara said the Hallucination Corrector reduced hallucination rates in enterprise AI systems to about 0.9%.

It works in conjunction with the company’s already widely used Hughes Hallucination Evaluation Model, which provides a way to compare responses to source documents and identify if statements are accurate at runtime.

The HHEM scores the answer against the source with a probability score between 1 and 0, where 0 means completely inaccurate – a total hallucination – and 1 for perfect accuracy. For example, a 0.98 means that there’s a 98% chance the answer is likely highly accurate. HHEM is available on Hugging Face and received over 250,000 downloads last month, making it one of the most popular hallucination detectors on the platform.

In the case of a factually inconsistent response, the Corrector provides a detailed output including an explanation of why the statement is a hallucination and a corrected version incorporating minimal changes for accuracy.

The company said it automatically uses the corrected output in summaries for end-users, but experts can use the full explanation and suggested fixes for testing applications to refine or fine-tune their models and guardrails to combat hallucinations. It can also show the original summary, but use corrections info to flag potential uses while offering the corrected summary as an optional fix.

In the case of LLM answers that fall into the category of misleading but not quite outright false, the Hallucination Corrector can work to refine the response to reduce its uncertainty core according to the customer’s settings.

Images: geralt/Pixabay, Vectara