LLM Response Accuracy Through Noun Phrase Collision Detection
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Solution Overview
Problem
Current language models, such as GPT-4 and GPT-3.5, suffer from high error rates and hallucinations when answering questions, making them unsuitable for production use due to their reliance on exact linguistic structures and inability to handle noun phrase collisions accurately.
Innovation Solution
Implementing a system that includes noun phrase collision detection, query splitting, and formatted fact models, along with bounded-scope deterministic neural networks and intelligent storage and retrieval systems to eliminate hallucinations and ensure accurate responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If LLMs are used for question answering, then response generation capability is improved, but error rate and hallucination increase
Solution Approach 1:
The patent introduces an intermediary NLP processing layer between the user query and the LLM. This intermediary layer includes noun phrase collision detection, query splitting, and formatted fact model correction that processes and cleans the input before it reaches the LLM, thereby reducing hallucinations and errors while preserving the LLM's response generation capability
Solution Approach 2:
The patent implements feedback mechanisms through formatted fact model correction interfaces that detect and correct errors in LLM responses. The system monitors output for hallucinations and noun phrase collisions, then provides corrections back to the user, creating a feedback loop that improves reliability without sacrificing response generation speed
2Productivity
If LLMs process queries directly, then processing speed is maintained, but noun phrase collision errors increase
Solution Approach 1:
The patent applies preliminary action by performing noun phrase collision detection and query splitting before the LLM processes the query. The system pre-processes the input to identify and resolve potential noun phrase conflicts, ensuring accurate tokenization and context understanding while maintaining efficient processing speeds through optimized NLP pipelines
3Measurement precision
If exact linguistic structure matching is used, then parsing accuracy is improved, but adaptability to variations decreases
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting the NLP processing parameters based on the input query. The noun phrase collision detection mechanism adapts its sensitivity and the query splitting strategy changes based on the specific linguistic patterns detected, allowing the system to maintain high parsing accuracy across diverse linguistic variations while preserving adaptability
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.