Logical Reasoning Layer for Explainable Generative AI
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Solution Overview
Problem
Conventional large language models (LLMs) lack the ability to perform logical reasoning, leading to inaccuracies and a lack of transparency in decision-making, particularly in fields requiring precision and explainability such as legal, medical, and financial services, and code generation, where the inability to trace and explain decisions complicates deployment and compliance.
Innovation Solution
A generative AI system that integrates logical reasoning capabilities, converting natural language or software code into logical statements, using a logical reasoning engine to evaluate and explain the outcomes in natural language, providing transparency and explainability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If analogy-based reasoning is used in conventional LLMs, then language understanding based on training data patterns is improved, but hallucinations occur when similarity scores do not represent logic or when similar words occur in different contexts
Solution Approach 1:
The system segments the reasoning process into two distinct components: analogy-based reasoning for language understanding and logical reasoning for decision-making. The LLM handles pattern recognition and language comprehension, while a separate logical reasoning engine handles deduction and fact-checking, preventing hallucinations from propagating to final decisions.
Solution Approach 2:
A logical reasoning engine acts as an intermediary between the LLM's pattern-based outputs and the final decision. This intermediary component translates natural language inputs into logical statements, performs rigorous logical deduction, and verifies facts, thereby mediating the transition from probabilistic language patterns to reliable logical conclusions.
2Ease of operation
If conventional LLMs rely on pattern recognition, then they can generate human-like text, but they cannot perform true logical reasoning or deduce facts not explicitly stated in input
Solution Approach 1:
The system merges two previously separate capabilities into a unified architecture: the LLM's natural language processing and generation capabilities are combined with a logical reasoning engine's deductive capabilities. This integration allows the system to both generate human-like text and perform rigorous logical reasoning, adapting to tasks requiring either or both capabilities.
3Reliability
If existing theorem solvers are used for math or geometry, then logical reasoning is achieved for those domains, but they require expertise beyond most users and cannot reason about natural language
Solution Approach 1:
The LLM serves as an intermediary that translates between natural language and logical notation. Users interact with the system in natural language, the LLM converts these inputs into formal logical statements that the theorem solver can process, and then translates the solver's outputs back into natural language explanations, making expert-level logical reasoning accessible to non-experts.
4Device complexity
If generative AI systems cannot trace and explain decisions, then deployment in regulated fields is simplified, but compliance with ethical standards and regulatory requirements cannot be ensured
Solution Approach 1:
The logical reasoning engine provides feedback mechanisms that trace and explain decision-making processes. Each logical deduction step is recorded and can be retrieved to explain why a particular conclusion was reached, providing audit trails for compliance verification while maintaining transparency about the reasoning process without adding significant deployment complexity.
Data Source
AI summary
Systems and methods are disclosed related to generative AI with logical reasoning. For example, an LLM may be used to convert statements such as natural language statements or lines of software code into logical statements in a logic specification language, a logical reasoning engine may be used to evaluate the logical statements, and an LLM may be used to explain the output of the logical reasoning engine in natural language (e.g., using a system that uses retrieval augmented generation (RAG) and/or a fine-tuned LLM). As such, the present techniques may be utilized to provide a generative AI system that can logically reason about, deduce new facts from, compute logical consequences of, and/or check the consistency of a set of statements such as natural language statements or software code, and provide a logical explanation of how the computation(s) were done.