人类注释的理由和可解释的文本分类:一项调查调查
Elize Herrewijnen1,2, Dong Nguyen1, Floris Bex1,3
1Department of Information & Computing Sciences, Utrecht University, Utrecht, Netherlands.
Frontiers in artificial intelligence
|June 10, 2024
概括
标注器的理由,或对数据标签的解释,提高机器学习模型的质量. 这些由人类产生的洞察力也有助于开发人工智能解释.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 自然语言处理自然语言处理.
背景情况:
- 数据注释对于训练机器学习模型至关重要.
- 了解注释背后的推理可以提高模型性能和可解释性.
- 当前的方法往往缺乏对分类决定的详细解释.
研究的目的:
- 调查收集和使用注释器理性的方法.
- 突出机器学习中人类注释理性的好处.
- 探索理性推理在推进可解释的人工智能的作用.
主要方法:
- 对涉及注释器理性的研究的文献综述.
- 对理性理由对数据质量影响的分析.
- 检查在模型开发和评估中使用推理的方法.
主要成果:
- 人类注释的理性解释显著提高了数据质量.
- 理性是增强机器学习模型的宝贵资源.
- 标注者推理启发了模型生成的推理的创建和评估.
结论:
- 对高质量的数据和改进的人工智能模型来说,注释器理性是必不可少的.
- 研究理性是开发更容易解释的人工智能的关键.
- 未来的工作应该集中在利用人类和机器学习的合理性.
相关概念视频
Reason and Intuition
6.4K
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...
6.4K
Aggregates Classification
317
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
317
How Data are Classified: Categorical Data
32.4K
A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
32.4K
Classification of Systems-II
140
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
140
Classification of Systems-I
179
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
179
Fundamental Attribution Error
12.8K
According to some social psychologists, people tend to overemphasize internal factors as explanations—or attributions—for the behavior of other people. They tend to assume that the behavior of another person is a trait of that person, and to underestimate the power of the situation on the behavior of others. They tend to fail to recognize when the behavior of another is due to situational variables, and thus to the person’s state. This erroneous assumption is...
12.8K


