Recent advancements in large language model (LLM) design have improved few-shot learning and reasoning capabilities. However, limitations remain when dealing with complex real-world contexts. To address this, retrieval augmented generation (RAG) systems integrating LLMs with scalable retrieval from knowledge graphs have shown promise. The LLM Compiler framework is being explored to optimize knowledge graph retrieval and reasoning.
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Orchestrating Efficient Reasoning Over Knowledge Graphs with LLM Compiler Frameworks
Recent advances in large language model (LLM) design have significantly improved few-shot learning and reasoning capabilities. However, these models still face challenges when dealing with complex real-world contexts involving extensive interconnected knowledge.
To address this, a promising approach has emerged in retrieval augmented generation (RAG) systems, which combine the adaptive learning strengths of LLMs with scalable retrieval from external knowledge sources like knowledge graphs (KGs).
Challenges of Reasoning Over Massive Knowledge Graphs
Addressing the challenges of reasoning over massive knowledge graphs involves leveraging computational strategies to balance efficiency, accuracy, and completeness in the analysis of large interconnected datasets.
Optimizing Cypher Queries for Mathematical Operations
Enhance the performance of Cypher queries by identifying opportunities, breaking down the process into parallel retrieval of groups, and providing example applications.
Planning Parallel Vector Searches
Implement parallel vector searches to efficiently navigate through graph-based data by analyzing questions for seed entities, exploring concurrent vector spaces, and continually retrieving nodes based on vector similarity.
Coordinating Usage of Graph Algorithms
Select and apply the most appropriate graph algorithms for specific queries by examining questions for algorithm selection, applying algorithms in a modular fashion, and resolving dependencies.
Knowledge Graphs as Modular LLM Tools
View knowledge graphs as modular tools that an LLM can orchestrate, with query engines over KGs becoming tools accessed by the LLM, graph algorithms and embeddings providing tool-level customization, and the LLM planner determining optimal multi-graph exploration strategies.
Structured Reasoning Powered by LLM Compilers
Enhance several facets of knowledge graph reasoning through techniques such as parallel exploration, modular retrieval, dependency management, recursive re-planning, ontology-aided planning, and integrating diverse data sources, aiming for remarkably efficient and precise navigation of extensive information spaces.
An Operating System for Knowledge Assimilation
Envision LLM Compiler techniques giving rise to a new paradigm of LLMs as operating systems overseeing diverse knowledge functions, scheduling for optimal concurrency between retrieval, reasoning, and learning functions and enabling modular expandability as new capabilities come online. The LLM Compiler would act as the key workflow orchestration framework interfacing between operating system and tools, handling all the underlying complexities of dependency resolution, concurrency optimization, and resource allocation on behalf of the LLM OS.
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