Logic Augmented Generation
Aldo Gangemi, Andrea Giovanni Nuzzolese

TL;DR
Logic Augmented Generation (LAG) combines Large Language Models and Semantic Knowledge Graphs to enhance interpretability, reliability, and reasoning in open-ended tasks like medical diagnostics and climate projections.
Contribution
This paper introduces LAG, a novel framework that integrates LLMs with SKGs to leverage the strengths of both for improved reasoning and interpretability.
Findings
LAG enables on-demand relation generation and tacit knowledge access.
LAG improves interpretability and reliability in complex tasks.
Initial analysis of LAG's properties and limitations.
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…
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Taxonomy
TopicsEmbedded Systems Design Techniques · Evolutionary Algorithms and Applications · Parallel Computing and Optimization Techniques
