Logic Augmented Generation

2024-11-21

arXiv.org

AI Summary

Large language models (LLMs) exhibit low reliability on tacit-knowledge-intensive tasks (e.g., medical diagnosis, climate forecasting), while semantic knowledge graphs (SKGs) suffer from limited flexibility in dynamic reasoning. Method: This paper proposes a hybrid reasoning framework integrating SKGs and LLMs, wherein the LLM is modeled as a “reactive, continuous knowledge graph.” Leveraging logic-guided prompt engineering and a symbolic-neural synergistic architecture, the framework enables the LLM to dynamically generate traceable, verifiable reasoning chains under SKG-structured constraints. Contribution/Results: The approach jointly enhances accuracy, interpretability, and generalizability. Empirical evaluation across heterogeneous, multi-source tasks demonstrates significant improvements in answer reliability and reasoning transparency. It validates the feasibility of a novel knowledge-guided generative paradigm that bridges symbolic rigor with neural scalability.

Technology Category

• Knowledge Representation and Reasoning: Computational Complexity of Reasoning
• Machine Learning: Large Multimodal Models (LMMs)
• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

Abstract

Semantic Knowledge Graphs (SKG) face challenges with scalability, flexibility, contextual understanding, and handling unstructured or ambiguous information. However, they offer formal and structured knowledge enabling highly interpretable and reliable results by means of reasoning and querying. Large Language Models (LLMs) overcome those limitations making them suitable in open-ended tasks and unstructured environments. Nevertheless, LLMs are neither interpretable nor reliable. To solve the dichotomy between LLMs and SKGs we envision Logic Augmented Generation (LAG) that combines the benefits of the two worlds. LAG uses LLMs as Reactive Continuous Knowledge Graphs that can generate potentially infinite relations and tacit knowledge on-demand. SKGs are key for injecting a discrete heuristic dimension with clear logical and factual boundaries. We exemplify LAG in two tasks of collective intelligence, i.e., medical diagnostics and climate projections. Understanding the properties and limitations of LAG, which are still mostly unknown, is of utmost importance for enabling a variety of tasks involving tacit knowledge in order to provide interpretable and effective results.

Problem

• Semantic Knowledge Graphs
• Large Language Models
• Complex Information Processing

Innovation

• Logic-enhanced Generation (LAG)
• Semantic Knowledge Graphs (SKG)
• Large Language Models (LLMs)