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In the race to deploy enterprise AI, one obstacle consistently blocks the path: hallucinations. These fabricated responses from AI systems have caused everything from legal sanctions for attorneys to companies being forced to honor fictitious policies.
Organizations have tried different approaches to solving the hallucination challenge, including fine-tuning with better data, retrieval augmented generation (RAG), and guardrails. Open-source development firm Oumi is now offering a new approach, albeit with a somewhat ‘cheesy’ name.
The company’s name is an acronym for Open Universal Machine Intelligence (Oumi). It is led by ex-Apple and Google engineers on a mission to build an unconditionally open-source AI platform.
On April 2, the company released HallOumi, an open-source claim verification model designed to solve the accuracy problem through a novel approach to hallucination detection. Halloumi is, of course, a type of hard cheese, but that has nothing to do with the model’s naming. The name is a combination of Hallucination and Oumi, though the timing of the release close to April Fools’ Day might have made some suspect the release was a joke – but it is anything but a joke; it’s a solution to a very real problem.
“Hallucinations are frequently cited as one of the most critical challenges in deploying generative models,” Manos Koukoumidis, CEO of Oumi, told VentureBeat. “It ultimately boils down to a matter of trust—generative models are trained to produce outputs which are probabilistically likely, but not necessarily true.”
How HallOumi works to solve enterprise AI hallucinations
HallOumi analyzes AI-generated content on a sentence-by-sentence basis. The system accepts both a source document and an AI response, then determines whether the source material supports each claim in the response.
“What HallOumi does is analyze every single sentence independently,” Koukoumidis explained. “For each sentence it analyzes, it tells you the specific sentences in the input document that you should check, so you don’t need to read the whole document to verify if what the [large language model] LLM said is accurate or not.”
The model provides three key outputs for each analyzed sentence:
A confidence score indicating the likelihood of hallucination.
Specific citations linking claims to supporting evidence.
A human-readable explanation detailing w …