Chatbot and LLM Response Accuracy via Noun Phrase Collision Detection
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Solution Overview
Problem
Existing language models and chatbots suffer from high error rates and hallucinations in question answering and summarization tasks, making them unreliable for production use.
Innovation Solution
Implement a system and method using noun phrase collision detection, query splitting, and formatted facts to correct errors and eliminate hallucinations, employing bounded-scope deterministic neural networks and intelligent storage and retrieval systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If large language models are used for question answering and summarization, then the system can process natural language queries, but the models produce high error rates and hallucinations making them unreliable
Solution Approach 1:
The patent segments the NLP task into distinct stages: noun phrase identification, collision detection, query splitting, and formatted fact generation. By dividing the complex LLM processing into separate manageable components, the system can identify and correct errors at specific points rather than relying on the entire LLM output, thereby reducing hallucinations while maintaining language processing capability.
Solution Approach 2:
The patent introduces an intermediary formatted fact representation layer between the user query and the LLM response. This intermediary structure serves as a mediator that validates and corrects noun phrase collisions before the LLM generates its response, acting as a buffer that prevents hallucinations from propagating through the system while allowing the LLM to maintain its natural language processing strengths.
2Measurement precision
If noun phrase collision detection is implemented to correct errors, then accuracy improves, but system complexity increases
Solution Approach 1:
The patent applies preliminary action by identifying and correcting noun phrase collisions before the LLM generates its final response. The system pre-processes the query to detect potential collisions and generate formatted facts in advance, so that when the LLM processes the query, the correction logic is already in place, reducing the need for complex post-processing and lowering overall system complexity.
Solution Approach 2:
The patent implements feedback mechanisms where the formatted fact generation process provides correction feedback to the LLM processing stages. By feeding back the corrected formatted facts through the system, the accuracy is improved while the feedback loop operates efficiently within the existing architectural framework, minimizing the increase in system complexity.
3Reliability
If formatted facts and bounded-scope deterministic neural networks are used, then hallucinations are eliminated, but the system requires precise deterministic processing
Solution Approach 1:
The patent applies parameter changes by transforming the LLM's probabilistic output into deterministic formatted fact representations. The system changes the representation parameters from vague probabilistic predictions to precise structured facts with clear noun phrase boundaries. This parameter transformation enables deterministic processing that eliminates hallucinations while the changes are implemented through standard NLP processing techniques, making the system manufacturable.
Solution Approach 2:
The patent creates a composite system that combines LLM capabilities with deterministic formatted fact generation and collision detection mechanisms. This composite architecture integrates the strengths of probabilistic LLM processing with the precision of deterministic fact validation, achieving hallucination elimination while the composite structure is built from existing NLP components that are relatively easy to implement.
Data Source
AI summary
Systems and methods are described for obtaining accurate responses from large language models (LLMs) and chatbots, including for question and answering, exposition, and summarization. These systems and methods accomplish these objectives via use of noun phrase avoiding processes such as a noun phrase collision detection process, a query splitting process, and a topical splitting process as well as by use of formatted facts, formatted fact model correction interfaces (FF MCIs), bounded-scope deterministic (BSD) neural networks, processes and methods, and intelligent storage and retrieval (ISAR) systems and methods. These systems and methods avoid and bypass noun phrase collisions and correct for errors caused by noun phrase collisions so that hallucinations are eliminated from LLM responses.