Using Non-AI Systems to Improve AI Text Generation

This patent describes a method where a traditional, rule-based computer system helps a Large Language Model (LLM) generate more accurate and reliable text by providing it with better context and fact-checking.

What it covers

This patent outlines a system where a non-LLM data processing system, which uses symbolic representations (like rules and facts), works alongside a Large Language Model (LLM). The non-LLM system takes an initial input, enhances it by accessing external knowledge sources, and then feeds this improved information to the LLM as a prompt. The LLM then uses this enhanced prompt to generate text that is more factually accurate, internally consistent, and aligned with real-world understanding. For example, if you ask an LLM about a historical event, the non-LLM system could first pull verified dates and facts from a database and provide them to the LLM, ensuring the LLM's response is grounded in reality.

What it doesn't cover

• —Methods where the LLM directly accesses external knowledge sources without an intermediary non-LLM system.
• —Systems that rely solely on statistical methods for data processing, without symbolic representations.
• —LLM outputs that are not fact-checked or verified for accuracy.
• —Methods where the non-LLM system does not enhance or augment the input before providing it to the LLM.
• —LLM-based systems that operate without any external data processing system.

The clever bit

The innovation lies in using a separate, symbolic reasoning system to act as an intelligent agent for the LLM, providing it with curated, fact-checked context rather than letting it rely solely on its training data, which can be outdated or biased.

Why it matters

As LLMs become more powerful, ensuring their outputs are accurate and reliable is crucial for many applications. This patent addresses the common problem of LLMs 'hallucinating' or generating incorrect information by introducing a structured way to guide their responses using more traditional, verifiable data processing.

Real-world examples

• 1.Customer service chatbots that provide accurate product information.
• 2.AI assistants that generate factual reports or summaries.
• 3.Tools that help researchers verify AI-generated hypotheses.
• 4.Systems for generating educational content with high factual integrity.