Large language models like ChatGPT can sometimes produce erroneous or hallucinated responses unsuitable for enterprise applications. These models generate answers probabilistically, providing human-like insights through immense statistical computation. However, the probability-based nature means complete accuracy isn’t guaranteed. Furthermore, lacking mechanisms for preserving truth, these models can fabricate false information. Enhancing the models themselves often fails to resolve such hallucinations.
This innovation uniquely integrates logic programming, which represents truth and falsehood, with large language models. The logic engine filters out incorrect responses using an enterprise knowledge base encoded in logic programming. It also composes optimized solutions matching user queries by combining model outputs with enterprise knowledge.
The software architecture called GPTProX parses model responses into logic programming predicates. These are validated against the knowledge base to eliminate hallucinations. By fusion with mathematical optimization, the system generates precise, customized responses grounded in reality. This principled approach promises to unlock enterprise applications for large language models by overcoming inaccuracies through structured knowledge representation and reasoning.
GPTProX represents a major advance in conversational AI by synergizing large language models like ChatGPT with validated knowledge bases, mathematical optimization, and live enterprise data systems. Through a patented approach, we have created an AI assistant that provides customizable, logically valid solutions tailored to your business needs. GPTProX is currently patent pending.
GPTProX validates conversational responses using logic programming in Prolog. Responses are parsed into verifiable logic predicates and checked against an enterprise knowledge base to filter out contradictions or false information.
An additional accuracy check is provided by modeling responses as mathematical morphisms using category theory. This technique identifies semantic inconsistencies that may indicate hallucinated information.
For queries requiring optimized solutions, GPTProX utilizes problem templates for techniques like linear programming, dynamic programming, and optimal control. Objectives are decomposed into components solvable by multi-agent math solvers.
Knowledge representation is enriched by interfacing with diverse enterprise data sources including ontologies, geographic information systems, financial databases, and more. This grounds conversations in the real-world operational context.
In summary, the patented GPTProX architecture brings together the strengths of conversational AI, logical reasoning, category theory, mathematical optimization, and live data to deliver robust enterprise solutions. Ask us how GPTProX can optimize and validate AI for your business needs.