100% Hallucination-Free AI

Hallucinations in large language models (LLMs) remains a critical barrier to the adoption of AI in enterprise and other high-stakes applications.

Despite advancements in RAG, state-of-the-art methods fail to achieve more than 80% accuracy, even when provided with relevant and accurate context.

Acurai introduces a groundbreaking solution that eliminates 100% of hallucinations in LLM-generated outputs.

Podcast: Acurai, Simplified

https://arxiv.org/abs/2412.05223

OpenAI Shifts Priorities
Solving QA Hallucinations No Longer The Focus

GPT-4.5 is the last Direct Response model. "Sam Altman has said that GPT-4.5 will be the last release in OpenAI's classic lineup." (MIT Technology Review).

Direct Response models are strongest on extractive fact-based question/answering (QA). Reasoning is strongest elsewhere. "GPT-4.5 doesn't think before it responds, which makes its strengths particularly different from reasoning models like OpenAI o1." (OpenAI). OpenAI has officially abandoned pursuit of 100% accuracy for extractive facts.

Acurai Fixes RAG

100% Elimination of Hallucinations on RAGTruth Corpus

Acurai: 0% Error Rate on Summarization

The Vectara Hallucination Leaderboard measures how well models summarize very short snippets of text — as short as seven words. This metric is meaningless for production.

For example, GPT-4 demonstrated a 1.8% hallucination rate when summarizing Vectara snippets; yet GPT-4 has a 46% hallucination rate when summarizing article-length texts. Acurai's summarization was tested on 500 BBC news articles. Acurai demonstrated a 0% hallucination rate when summarizing article-length text.

Acurai: 0% Error Rate in Fact Extraction

How Acurai Works

LLMs are constructed from multiple layers. The input (or prompt) passes through these layers to generate the output. Likewise, each layer contains many neurons, and each neuron has values it learned during training (e.g. weights and biases).

Our Noun-Phrase Dominance model says that neurons don’t operate on their own, but rather self-organize during training around noun phrases.

Both OpenAI and Anthropic recently discovered this to be the empirical truth—the actual way that LLMs operate under the hood. [1]

In order to eliminate hallucinations, you must eliminate Noun-Phrase Collisions. It is literally impossible to create a hallucination-free pipeline without achieving this.

In order to avoid Noun-Phrase Collisions, you must send all inputs to the LLM in the form of Fully Formatted Facts.[2]

These three insights are the foundation for hallucination-free AI. The key is to eliminate the causes of hallucinations before the LLM, not after.

Additional steps are required to maintain 100% accuracy throughout the AI pipeline. But without this foundation, 100% accuracy is impossible.

[1]

1 big thing: Opening AI's black box

https://www.axios.com/newsletters/axios-ai-plus-78a63c60-60a7-11ef-a142-0192710805a0.html

Notice that the self-organization is not around verbs, adjectives, adverbs, etc. In stark contrast, the neurons self organize around “places, people, objects and concepts.” In other words, the neurons self organize around noun phrases—just as the Noun-Phrase Dominance Model stated.

Noun phrase groupings (i.e. features) cluster “near related terms” affirming the existence of Noun-Phrase Routes—just as the Noun-Phrase Dominance Model stated.

[2]

A Fully Formatted Fact is a sentence that is completely unambiguous to an LLM from a noun-phrase perspective. It is also the simplest form of sentence: one with a single, independent clause. For example:

“Calcium is a silver-grey metal.”

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