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Aprendizaje Contrastivo Basado en Prompts para Extracción de Relaciones de Disparo Cero

Xueyi Zhong1, Liye Zhao2, Licheng Peng2

  • 1School of Finance, Southwestern University of Finance and Economics, Chengdu 611130, China.

Entropy (Basel, Switzerland)
|January 28, 2026
PubMed
Resumen
Este resumen es generado por máquina.

Este estudio presenta el Aprendizaje Contrastivo Basado en Prompts para la Extracción de Relaciones (PCRE) para mejorar el aprendizaje de disparo cero aprovechando modelos de lenguaje preentrenados. PCRE mejora las representaciones semánticas para una mejor extracción de relaciones no vistas.

Palabras clave:
aprendizaje contrastivoaprendizaje basado en promptsextracción de relacionesconfiguración de disparo cero

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Área de la Ciencia:

  • Procesamiento del Lenguaje Natural
  • Inteligencia Artificial
  • Aprendizaje Automático

Sus antecedentes:

  • La extracción de relaciones es crucial para la adquisición de conocimiento, pero requiere una anotación de datos exhaustiva.
  • El aprendizaje de disparo cero aborda los costos de anotación al permitir que los modelos identifiquen relaciones no vistas.
  • Los métodos actuales de disparo cero tienen dificultades con diversas formulaciones de tareas, lo que lleva a un rendimiento subóptimo.

Objetivo del estudio:

  • Desarrollar un enfoque novedoso para la extracción de relaciones de disparo cero explotando el conocimiento de los modelos de lenguaje preentrenados.
  • Mejorar las capacidades de representación semántica de los modelos de extracción de relaciones para relaciones no vistas.
  • Introducir un marco de aprendizaje contrastivo basado en prompts (PCRE) para mejorar la extracción de relaciones de disparo cero.

Principales métodos:

  • Aprovechar el conocimiento semántico de los modelos de lenguaje preentrenados a través de la optimización de prompts.
  • Aumentar las instancias con diversas plantillas de prompts para crear vistas duales para el aprendizaje contrastivo.
  • Implementar un objetivo de contraste entre instancias y descripciones para extraer conocimiento relacional de las descripciones de texto.

Principales resultados:

  • El método PCRE propuesto supera significativamente las bases de referencia existentes de última generación en la extracción de relaciones de disparo cero.
  • Los resultados experimentales demuestran la robustez de PCRE en varios conjuntos de datos y configuraciones de entrenamiento.
  • PCRE mejora eficazmente la capacidad del modelo para diferenciar entre relaciones vistas y no vistas.

Conclusiones:

  • PCRE ofrece una dirección prometedora para mejorar la extracción de relaciones de disparo cero al utilizar eficazmente modelos de lenguaje preentrenados.
  • La estrategia de aprendizaje contrastivo basado en prompts mejora las representaciones semánticas, lo que lleva a un rendimiento superior en la identificación de relaciones novedosas.
  • La robustez del método sugiere su amplia aplicabilidad en tareas de extracción de conocimiento del mundo real.