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Ajuste de instrucciones orientadas a gráficos de grandes modelos de lenguaje para la minería de gráficos genéricos

Yanchao Tan, Hang Lv, Pengxiang Zhan

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    MuseGraph integra las redes neuronales de gráficos (GNNs) y los grandes modelos de lenguaje (LLMs) para la minería de gráficos versátil. Este modelo de base mejora la precisión en diversas tareas de gráficos y conjuntos de datos sin capacitación específica para la tarea.

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    Área de la Ciencia:

    • Inteligencia artificial
    • Aprendizaje automático
    • Ciencia de los datos

    Sus antecedentes:

    • Las redes neuronales de gráficos (GNNs) tradicionalmente requieren capacitación específica para la tarea.
    • Los grandes modelos de lenguaje (LLM) son prometedores, pero están poco explorados para la minería de gráficos genéricos.
    • Se necesita un modelo unificado para diversas tareas de gráficos y conjuntos de datos.

    Objetivo del estudio:

    • Desarrollar un nuevo marco, MuseGraph, que integre GNN y LLM para la minería de gráficos versátiles.
    • Permitir que un solo modelo maneje múltiples tareas de gráficos y conjuntos de datos simultáneamente.
    • Mejorar las capacidades generativas de los LLM al tiempo que mejora el rendimiento de la minería de gráficos.

    Principales métodos:

    • Desarrolló una descripción gráfica compacta para la eficiencia del token de lenguaje.
    • Propuso un mecanismo diverso de generación de instrucciones con cadena de pensamiento (CoT) para destilar el razonamiento de LLM.
    • Diseñó una estrategia de ajuste de instrucciones consciente de gráficos para evitar el olvido catastrófico y promover la mejora mutua.

    Principales resultados:

    • MuseGraph demostró mejoras significativas en cinco tareas gráficas y diez conjuntos de datos.
    • El marco mejora la precisión en las tareas orientadas a gráficos.
    • Se observaron mejores capacidades de generación de LLM junto con el rendimiento de minería de gráficos.

    Conclusiones:

    • MuseGraph ofrece un poderoso modelo de base para la minería de gráficos genéricos.
    • La integración de las GNN y las LLM presenta una dirección prometedora para la investigación futura.
    • Este enfoque aborda las limitaciones de los GNN tradicionales y amplía las aplicaciones de LLM.