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Cultural frameworks for understanding the self are often categorized into two broad orientations: individualism and collectivism. These paradigms influence how people define themselves, relate to others, and interpret their social worlds. Each orientation offers distinct perspectives on autonomy, responsibility, and the role of the individual within a community.Individualistic CulturesIn individualistic cultures like North America and Western Europe, identity is understood as autonomous and...
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Social psychology examines how the real or imagined presence of others influences individuals' thoughts, feelings, and behaviors. A key concept in this field is the role of social context in shaping behavior. The same individual may act differently depending on the social setting, due to the varying expectations and norms associated with each environment. This context-dependent behavior illustrates the influence of social roles, which prescribe appropriate conduct in specific situations.Social...
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Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
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Doble desviación para el aprendizaje en contexto ruidoso para la generación de texto.

Siqi Liang1, Sumyeong Ahn2, Paramveer S Dhillon1

  • 1University of Michigan, Ann Arbor, MI, USA.

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Resumen
Este resumen es generado por máquina.

Este estudio introduce un nuevo método para identificar datos ruidosos para el aprendizaje en contexto (ICL). Nuestro enfoque detecta con precisión las demostraciones defectuosas, incluso con un alto nivel de ruido, mejorando el rendimiento de ICL.

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Área de la Ciencia:

  • La inteligencia artificial es la inteligencia artificial.
  • Procesamiento del lenguaje natural.

Sus antecedentes:

  • El aprendizaje en contexto (ICL) depende de datos de alta calidad.
  • Los métodos actuales para detectar datos ruidosos utilizando la perplejidad fallan cuando los niveles de ruido son altos.

Objetivo del estudio:

  • Abordar las limitaciones de los métodos de detección de ruido basados en la perplejidad existentes en ICL.
  • Desarrollar una métrica robusta para identificar la limpieza de la muestra independientemente de los niveles de ruido del cuerpo.

Principales métodos:

  • Reexaminó los sesgos de perplejidad de las anotaciones y el conocimiento del dominio LLM.
  • Se introdujo un marco de doble desviación utilizando vecinos sintetizados para corregir la perplejidad.
  • Desarrolló un robusto puntaje de limpieza de la muestra.

Principales resultados:

  • La puntuación de limpieza de la muestra propuesta identifica con precisión las muestras ruidosas a través de diferentes proporciones de ruido.
  • El método demuestra capacidades superiores de detección de ruido en comparación con los enfoques existentes.
  • El rendimiento de ICL utilizando el método propuesto es comparable al uso de un conjunto de datos completamente limpio.

Conclusiones:

  • El marco de doble desviación supera efectivamente los sesgos de perplejidad en entornos ruidosos de ICL.
  • El puntaje de limpieza de la muestra proporciona una medida confiable de la calidad de los datos.
  • Este enfoque ofrece un rendimiento robusto incluso con ratios de ruido extremadamente altos en los datos de demostración.