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相关概念视频

Reasoning01:30

Reasoning

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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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Inductive Reasoning00:59

Inductive Reasoning

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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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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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In psychology, concepts can be divided into two categories: natural and artificial. Natural concepts are formed through direct or indirect experiences. For example, consider the concept of snow. If you live in a place with regular snowfall, such as Essex Junction, Vermont, you know snow through direct experiences. You’ve seen it fall, touched it, shoveled it, and played in it. You recognize its texture, appearance, and even its smell. In contrast, if you live on an island like Saint...
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Language and Cognition01:27

Language and Cognition

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Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.
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相关实验视频

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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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基于语言的推理图表神经网络用于常识问题回答.

Meng Yang1, Yihao Wang1, Yu Gu1

  • 1School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China; Key Laboratory of Machine Intelligence and Advanced Computing (SYSU), Ministry of Education, Guangzhou 510006, China.

Neural networks : the official journal of the International Neural Network Society
|November 3, 2024
PubMed
概括

本研究介绍了基于语言的推理图神经网络 (LBR-GNN),通过整合外部知识来改善常识问题答案. 通过有效地捕获文本和知识图之间的上下文信息,LBR-GNN提高了推理性能.

关键词:
常识QA是一种常识QA.外部知识 外部知识基于语言的推理 基于语言的推理

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科学领域:

  • 人工智能的人工智能
  • 自然语言处理自然语言处理.
  • 机器学习 机器学习

背景情况:

  • 语言模型 (LMs) 在问答 (CSQA) 中对常识推理至关重要.
  • 增加LM参数带来减少的回报;外部知识集成是关键.
  • 图形神经网络 (GNN) 提高了性能,但与多种知识来源和文本知识背景作斗争.

研究的目的:

  • 建议基于语言的推理图神经网络 (LBR-GNN) 改进CSQA.
  • 解决利用多样化的知识和捕捉文本知识背景的挑战.
  • 增强常识理解和推理能力.

主要方法:

  • 使用语言模型表示问题,答案和外部知识.
  • 将外部知识调整成一个一致的文本形式,并用LM编码它.
  • 构建一个具有语言级边缘表示和GNN更新和推理的新型边缘聚合方法的GNN.

主要成果:

  • 与最先进的方法相比,LBR-GNN在CommonsenseQA数据集上显示了超过5%的性能提升.
  • 该方法通过同样数量的额外参数实现了这种改进.
  • 在CommonsenseQA-IH和OpenBookQA数据集上也观察到有效的性能.

结论:

  • LBR-GNN有效地整合了外部知识,以提高常识性问题的答案.
  • 提出的基于语言的GNN方法成功地捕获了文本和知识之间的上下文信息.
  • LBR-GNN为推进AI推理能力提供了一个有希望的方向.