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相关概念视频

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The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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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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Although black holes were theoretically postulated in the 1920s, they remained outside the domain of observational astronomy until the 1970s.
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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.
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相关实验视频

Updated: Sep 18, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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目标:大语言模型驱动的可解释的仇恨言论检测.

Muhammad Haseeb Hashir1, Memoona1, Sung Won Kim2

  • 1Information and Communication Engineering, Yeungnam University, Gyeongsan, Gyeongbuk, Republic of South Korea.

PeerJ. Computer science
|June 26, 2025
PubMed
概括
此摘要是机器生成的。

本研究引入了一种新的方法,用于在线检测仇恨言论,使用大型语言模型 (LLM) 来解释他们的决定. 这种方法提高了内容调节的准确性和透明度.

关键词:
仇恨言论就是一种仇恨言论.大型语言模型.推理提取的提取方法社交媒体 社交媒体

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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.
  • 计算社会科学 计算社会科学

背景情况:

  • 在社交媒体上,用户生成的内容给大规模检测仇恨言论带来了挑战.
  • 传统的手动调节是不切实际的;需要自动化解决方案.
  • 现有的深度学习模型缺乏透明度,限制了它们在微妙内容调节中的实用性.

研究的目的:

  • 使用先进的语言模型开发透明的仇恨言论检测系统.
  • 利用大型语言模型 (LLM) 进行可解释的内容调节.

主要方法:

  • 使用了Mistral-7B,这是一个最先进的语言模型.
  • 开发了一个框架,LLM可以为仇恨言论的检测产生明确的理由.
  • 将LLM生成的理性整合到专业分类器中,以进行可解释的调节.

主要成果:

  • 纳入LLM生成的解释显著提高了仇恨言论检测的解释性.
  • 该方法改善了识别煽动性和歧视性言论的准确性.
  • 该系统为每个调节决定提供了明确的分析理由.

结论:

  • 通过LLM生成的推理提高了自动检测仇恨言论的透明度和准确性.
  • 这种方法解决了在内容调节中对可解释AI的关键需求.
  • 该框架为细微而可扩展的在线内容调节提供了一个实用的解决方案.