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相关概念视频

Retrieval01:12

Retrieval

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Retrieval is the process of getting information out of memory storage and back into conscious awareness. This ability is essential for daily tasks like brushing hair and teeth, driving to work, and performing job duties. Retrieval occurs in three ways: recall, recognition, and relearning.
Recall involves accessing information without cues, such as during an essay test, where individuals must retrieve facts and concepts from memory unaided. Another example is remembering the name of a colleague...
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ER Retrieval Pathway01:45

ER Retrieval Pathway

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In the secretory pathway, vesicles transport proteins from one cellular compartment to another in forward transport to deliver the protein to its correct location. Occasionally, misfolded proteins and incorrect proteins escape their original compartments, and a retrieval pathway is used to return the escaped proteins to their original compartment.
The ER uses many checkpoints to prevent the entry of incorrectly folded or a resident protein as cargo onto a transport vesicle. These mechanisms...
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Associative Learning01:27

Associative Learning

298
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
298
Deductive Reasoning01:16

Deductive Reasoning

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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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Storage01:23

Storage

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A schema is a mental framework that helps individuals organize and interpret information. Schemata, formed from previous experiences, influence how we process new information: how we encode it, the inferences we make, and how we retrieve it. For instance, a schema for what a typical classroom looks like might include desks, a teacher's desk, a whiteboard, and students in such an environment. This expectation helps us quickly understand and navigate new classrooms without needing to analyze...
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Elaborative Rehearsals01:07

Elaborative Rehearsals

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Elaborative rehearsal is a crucial cognitive strategy that strengthens information encoding in long-term memory by making meaningful connections between new data and pre-existing knowledge. This approach contrasts with maintenance rehearsal, which involves simple repetition without delving into the significance of the information. While maintenance rehearsal might temporarily keep information active in short-term memory, it is less effective for long-term retention.
The effectiveness of...
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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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在解码器中检索对生成模型有好处,可以解释复杂的问题,回答问题.

Jianzhou Feng1, Qin Wang1, Huaxiao Qiu1

  • 1School of Information Science and Engineering, Yanshan University, Qinhuangdao, 066004, China.

Neural networks : the official journal of the International Neural Network Society
|November 7, 2024
PubMed
概括

本研究介绍了Retrieval In Decoder (RID),这是一个无监督的框架,将检索集成到生成模型中,以减少事实幻觉. RID 增强了大型和小型语言模型,优于现有方法.

关键词:
可解释的人工智能生成式解码是指生成式解码.信息检索 信息检索知识的蒸知识的蒸.问答问题 回答问题

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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:

  • 大规模语言模型 (LLM) 是有前途的,但却存在事实上的幻觉.
  • 目前的提取增强方法由于单独的提取器和发电机组件而存在局限性.
  • 监督训练限制了现有的提取增强方法中的发电机能力.

研究的目的:

  • 提出一个无监督的框架,Retrieval In Decoder (RID),用于多颗粒度解码.
  • 将检索直接集成到生成模型的解码过程中.
  • 通过强化学习增强小规模语言模型 (SLM) 的适应性解释生成.

主要方法:

  • 开发了无监督检索解码器 (RID) 框架.
  • 根据检索结果实现了基于解码细粒度的动态调整.
  • 引入了强化学习驱动的知识蒸,用于自适应性解释生成.

主要成果:

  • RID框架在六个公共基准指标中表现出卓越的表现.
  • 超过了流行的LLM和现有的检索增强方法.
  • 验证了LLM和SLM的有效性和可扩展性.

结论:

  • 在生成模型中,RID有效地减轻了事实幻觉.
  • 该框架显示了模型性能和适用性的显著改进.
  • RID为增强语言模型生成提供了可扩展和有效的解决方案.