LLM Theory Resolution for Verifiable Commonsense Reasoning
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Solution Overview
Problem
Existing large language models (LLMs) suffer from hallucinations, lack transparency in reasoning processes, and are difficult to debug and repair, limiting their reliability in critical applications due to opaque and unverifiable logical steps.
Innovation Solution
A multi-component framework that includes an LLM-based parser, theory resolution engine, and repair module to translate natural language queries into formal logical structures, generate verifiable proofs, and prioritize corrected information, ensuring logical soundness and transparency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If LLMs are used for commonsense reasoning, then text generation and logical inference capabilities are improved, but hallucinations and factual correctness deteriorate
Solution Approach 1:
The patent introduces an intermediary verification mechanism that acts as a mediator between the LLM's generated reasoning steps and the final conclusion. This intermediary component checks each reasoning step against the knowledge base and logical rules, preventing hallucinations while maintaining the LLM's reasoning capabilities. The intermediary validates factual correctness at each step of the reasoning process.
2Reliability
If LLM reasoning process is made transparent, then verifiability and debuggability are improved, but system complexity increases
Solution Approach 1:
The patent segments the reasoning process into discrete, verifiable steps that can be individually inspected and validated. Each reasoning step is broken down into atomic operations that can be checked against the knowledge base independently. This segmentation makes the overall complex system manageable and verifiable without requiring complete system redesign.
Solution Approach 2:
The patent implements feedback mechanisms where each reasoning step produces observable outputs that can be verified against expected results. The system provides feedback loops that allow debugging by identifying which specific step deviates from correct reasoning. This feedback structure enables verifiability without proportionally increasing complexity.
3Reliability
If repair mechanisms are added to correct reasoning errors, then reliability is improved, but ease of repair and implementation complexity worsen
Solution Approach 1:
The patent performs preliminary validation of reasoning steps before they are committed to the final conclusion. By checking factual correctness and logical validity in advance, the system prevents errors from propagating. This preliminary action reduces the need for complex post-hoc repair mechanisms, as most errors are caught before they become permanent failures.
Solution Approach 2:
The patent replaces manual debugging and repair processes with automated verification and correction mechanisms. The system automatically detects reasoning errors through logical validation and factual checking, then applies predefined correction rules without requiring manual intervention. This substitution of automated mechanisms for manual repair simplifies the ease of repair despite adding verification complexity.
Data Source
AI summary
A method for controlling an artificial intelligence (AI) device can include receiving a query including natural language, parsing, by a large language model-based parser, the query into a formal logical structure, initiating, by a theory resolution engine, a proof process to generate a logical proof for the query, and selecting, by a rules selector, a plurality of selected rules from a knowledge base that are relevant to an active logical clause in the proof process. Also, the method can further include generating, by the theory resolution engine, one or more logical clauses for the logical proof by applying one or more of the plurality of selected rules until a condition is met, and in response to meeting the condition, outputting an answer to the query and the logical proof corresponding to the answer.