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Reason and Intuition01:37

Reason and Intuition

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The human brain processes information for decision-making using one of two routes: an intuitive system and a rational system (Epstein, 1994; popularized by Kahneman, 2011 as System 1 and System 2, respectively). The intuitive system is quick, impulsive, and operates with minimal effort, relying on emotions or habits to provide cues for what to do next, while the rational system is logical, analytical, deliberate, and methodical. Research in neuropsychology suggests that the...
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Reasoning01:30

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Reasoning is the action of thinking about something in a logical, sensible way. It is integral to problem-solving, decision-making, and critical thinking. Reasoning can be inductive or deductive. Reasoning involves transforming information into conclusions, which is essential for problem-solving, decision-making, and critical thinking.
Inductive reasoning involves deriving generalizations from specific observations. This type of reasoning helps form beliefs about the world. For example,...
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Deductive Reasoning01:16

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Deductive reasoning, or deduction, is the type of logic used in hypothesis-based science. In deductive reasoning, the pattern of thinking moves in the opposite direction as compared to inductive reasoning, which means that it uses a general principle or law to predict specific results. From those general principles, a scientist can deduce and predict the specific results that would be valid as long as the general principles are valid.
For example, a researcher can deduce specific predictions...
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Language01:16

Language

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Language is a unique communication system that uses words and systematic rules to organize and transmit information. Unlike other forms of communication, which may involve postures, movements, odors, or vocalizations, language relies on symbols and grammar. This makes human communication distinct from that of other species, who also communicate but do not use language in the same way humans do.
Corballis and Suddendorf (2007) and Tomasello and Rakoczy (2003) highlight the role of language in...
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Inductive Reasoning00:59

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Inductive reasoning is a form of logical thinking that uses related observations to arrive at a general conclusion. It is uncertain and operates in degrees to which the conclusions are credible. As such, inductive arguments can be weak or strong, rather than valid or invalid, and conclusions can be used to formulate testable, falsifiable hypotheses.
Inductive reasoning is common in descriptive science. A life scientist makes observations and records them. This data can be qualitative or...
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Self Within Cultural Contexts01:30

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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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Exploring the Role of Deontic Reasoning and World Knowledge in Wason´s Selection Task
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Los modelos de lenguaje grandes son profesores en contexto para el razonamiento del conocimiento

Jiachen Zhao1, Zonghai Yao2, Zhichao Yang2

  • 1Northeastern University.

Findings of ACL. EMNLP. Conference on Empirical Methods in Natural Language Processing
|January 28, 2026
PubMed
Resumen
Este resumen es generado por máquina.

Los modelos de lenguaje grandes (LLM) pueden actuar como profesores eficaces para la enseñanza en contexto (ICT), superando a los instructores humanos. Los métodos de autoexplicación y retroalimentación mejoran la ICT basada en LLM al alinear las explicaciones del modelo profesor y del modelo alumno.

Palabras clave:
enseñanza en contextomodelos de lenguaje grandesautoexplicaciónretroalimentaciónrazonamiento del conocimiento

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Área de la Ciencia:

  • Inteligencia Artificial
  • Procesamiento del Lenguaje Natural
  • Aprendizaje Automático

Sus antecedentes:

  • La enseñanza en contexto (ICT) se basa en ejemplos creados por humanos, que son costosos y variables.
  • Los modelos de lenguaje grandes (LLM) ofrecen una alternativa potencial para crear demostraciones en contexto.

Objetivo del estudio:

  • Investigar si los LLM pueden servir como profesores más eficaces que los humanos en ICT.
  • Desarrollar métodos novedosos para mejorar la ICT basada en LLM.

Principales métodos:

  • Proponer la autoexplicación: usar las explicaciones autogeneradas de un LLM como demostraciones en contexto.
  • Validar la hipótesis de especificidad de la codificación: los ejemplos del profesor deben coincidir con los datos de entrenamiento del alumno.
  • Introducir la retroalimentación: alinear los LLM del profesor y del alumno para mejorar el rendimiento de la ICT.

Principales resultados:

  • La autoexplicación supera significativamente a los ejemplos creados por humanos y a otras líneas de base.
  • Las explicaciones que se asemejan a las autoexplicaciones del LLM del alumno sirven como mejores demostraciones.
  • La retroalimentación permite que un LLM más pequeño enseñe a uno más grande, superando a los profesores humanos en precisión.

Conclusiones:

  • Los LLM pueden ser profesores en contexto superiores en comparación con los humanos.
  • Los métodos propuestos de autoexplicación y retroalimentación mejoran la ICT basada en LLM.
  • La alineación entre las explicaciones del LLM del profesor y del alumno es crucial para una ICT eficaz.