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Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
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Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
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SR-LLM: Un marco de regresión simbólica incremental impulsado por generación aumentada por recuperación basada en LLM

Zelin Guo1, Siqi Wang1, Yonglin Tian2

  • 1Department of Automation, Tsinghua University, Beijing 100084, China.

Proceedings of the National Academy of Sciences of the United States of America
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Resumen

La regresión simbólica (SR) utilizando modelos de lenguaje grandes (LLM) y generación aumentada por recuperación permite el aprendizaje incremental. Este marco SR-LLM utiliza eficazmente el conocimiento previo para descubrir modelos analíticos complejos e interpretables a partir de datos.

Palabras clave:
modelos de lenguaje grandesgeneración aumentada por recuperaciónregresión simbólica

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Área de la Ciencia:

  • Inteligencia Artificial
  • Aprendizaje Automático
  • Ciencia de Datos

Sus antecedentes:

  • La regresión simbólica (SR) es crucial para descubrir modelos analíticos a partir de datos.
  • Los algoritmos de SR existentes tienen dificultades con los vastos espacios de búsqueda, lo que limita el descubrimiento de expresiones complejas.
  • Los avances en el aprendizaje profundo han renovado el interés en la SR para la modelización analítica.

Objetivo del estudio:

  • Introducir SR-LLM, un marco novedoso de SR que aprovecha los modelos de lenguaje grandes (LLM) y la generación aumentada por recuperación para el aprendizaje incremental.
  • Mejorar el descubrimiento de expresiones analíticas complejas e interpretables integrando el conocimiento previo.
  • Aplicar el marco a dominios desafiantes como el análisis del comportamiento humano de seguimiento de automóviles.

Principales métodos:

  • SR-LLM integra la generación aumentada por recuperación con LLM para el aprendizaje incremental.
  • El marco compone información previa en grupos simbólicos utilizando LLM.
  • El aprendizaje por refuerzo profundo combina estos grupos para formular expresiones analíticas complejas.

Principales resultados:

  • SR-LLM demuestra un rendimiento superior en puntos de referencia estándar de SR.
  • El marco redescubre con éxito modelos conocidos de seguimiento de automóviles a partir de datos empíricos.
  • Se descubrieron nuevos modelos analíticos para el comportamiento humano de seguimiento de automóviles, que muestran tanto eficacia como interpretabilidad.

Conclusiones:

  • SR-LLM utiliza eficientemente el conocimiento previo y la exploración pasada para la regresión simbólica.
  • El marco facilita el descubrimiento de modelos analíticos complejos y comprensibles para el ser humano.
  • SR-LLM ofrece un enfoque potente para el descubrimiento científico en diversos dominios, incluido el análisis del comportamiento.