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相关概念视频

Language and Cognition01:27

Language and Cognition

442
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.
442
Per-Unit Sequence Models01:26

Per-Unit Sequence Models

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An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
116
Language Development01:22

Language Development

450
Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
The critical period for language acquisition suggests that the ability to acquire language is at its peak early in life. As people age, this proficiency decreases. Language development begins very...
450
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

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Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
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Associative Learning01:27

Associative Learning

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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...
576
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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相关实验视频

Updated: Sep 12, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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利用多式联运大型语言模型进行多式联运顺序推.

Zhaoliang Wang1,2, Baisong Liu3, Weiming Huang1

  • 1Faculty of Information Science and Engineering, Ningbo University, Ningbo, 315211, People's Republic of China.

Scientific reports
|August 7, 2025
PubMed
概括

多式联网大型语言模型 (MLLMs) 通过融合多式联网功能和建模动态用户偏好来增强顺序推系统. MLLM-SRec通过利用MLLM来提高建议的准确性和稳定性,以获得更好的跨模式理解.

关键词:
多模式的大型语言模型关于多式联运的建议推系统是一个推系统.连续推的建议.

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科学领域:

  • 人工智能的人工智能
  • 计算机科学 计算机科学
  • 机器学习 机器学习

背景情况:

  • 传统的多式联运推系统在信息利用不足,多式联运特征识别有限,动态偏好建模无效等方面扎.
  • 现有的方法通常依赖于单模式数据,无法捕捉跨模式偏好和用户兴趣在连续交互中的演变.
  • 多模式大语言模型 (MLLMs) 提供先进的跨模式理解和世界知识,为推系统增强提供了一个有前途的途径.

研究的目的:

  • 引入 MLLM-SRec,一种利用 MLLM 来解决当前多式联运推系统的局限性的新型顺序推架构.
  • 开发一种多式联络功能融合机制,用于统一的项目表示,使视觉和文本对齐,同时减轻跨式联络差异和噪音.
  • 为动态用户偏好建模设计一个时间意识模块,并将其与思维链提示进行集成,以实现有效的知识传输.

主要方法:

  • 开发了一种使用MLLM的多式特征融合机制,以创建统一的语义项目表示,确保视觉和文本数据之间的语义对齐.
  • 实现了一个时间意识的用户行为理解模块,以捕捉用户偏好在顺序交互数据中的动态演变.
  • 员工监督的微调与多步思维链相结合,促使优化从预先训练的MLLM到推任务的知识转移.

主要成果:

  • 拟议的MLLM-SRec架构在四个基准数据集中实现了与最先进的基线相比的显著改进.
  • 该方法大大提高了推结果的精度.
  • 在多式联运顺序推场景中,MLLM-SRec表现出卓越的稳定性和适应性.

结论:

  • 在顺序推中,MLLM-SRec有效地解决了多式联运特征识别和动态偏好建模的挑战.
  • 该架构验证了MLLM在通过改进多式联络交互数据利用来推进顺序推任务方面的巨大潜力.
  • 这些发现为多式联络序列推研究提供了新的方法见解,并强调了MLLM集成的好处.