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Higher Mental Functions of the Brain: Language01:10

Higher Mental Functions of the Brain: Language

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Language is a system of communication that allows the expression of thoughts, ideas, and feelings. The brain processes language in both hemispheres.
Language formation and comprehension take place in the dominant hemisphere. The dominant hemisphere is responsible for understanding the meaning of spoken, written, or sign language, as well as the ability to communicate. For most people, the left hemisphere is the dominant one. The right hemisphere, then, gives tone and emotional context to the...
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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

Updated: Sep 11, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

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一个可扩展的框架,通过跨域生成和幻觉检测来评估多种语言模型.

Sorup Chakraborty1, Rajesh Chowdhury1, Sourov Roy Shuvo1

  • 1School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, 751024, Odisha, India.

Scientific reports
|August 16, 2025
PubMed
概括

一个新的基准测试框架,MultiLLM-Chatbot,评估了专门领域的大型语言模型 (LLM). LLAMA-3.3-70B在农业,生物学,经济,物联网和医疗领域表现出卓越的表现.

关键词:
偏差检测 偏差检测 偏差检测弹性搜索 弹性搜索幻觉检测 检测 幻觉检测大型语言模型 (LLM)多域基准测试多域基准测试检索增强生成 (RAG) 是指检索增强生成.语义相似性 语义相似性

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Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
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Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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相关实验视频

Last Updated: Sep 11, 2025

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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

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Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
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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.
  • 信息检索 信息检索

背景情况:

  • 大型语言模型 (LLM) 具有先进的检索增强生成 (RAG) 系统.
  • 在特定领域的LLM应用中,语义相似性,偏见和幻觉等挑战仍然存在.

研究的目的:

  • 介绍MultiLLM-Chatbot,一个可扩展的基于RAG的基准测试框架.
  • 评价五个受欢迎的LLM (GPT-4-Turbo,CLAUDE-3.7-Sonnet,LLAMA-3.3-70B,DeepSeek-R1-Zero,Gemini-2.0-Flash) 的使用情况.
  • 评估五个领域的LLM绩效:农业,生物学,经济,物联网 (IoT) 和医学.

主要方法:

  • 从50篇同行评审论文中生成了250个标准化查询.
  • 提取和细分PDF文本,嵌入它们,并在Elasticsearch中进行索引.
  • 分析了1250个模型响应,使用了等号相似性,VADER情绪分析,TF-IDF和命名实体识别 (NER).

主要成果:

  • 总体而言,LLAMA-3.3-70B成为了表现最好的模型.
  • 在所有五个评估领域中,LLAMA-3.3-70B领先.
  • 该框架为特定领域的LLM基准测试提供了可重复的管道.

结论:

  • 多LLM-聊天机器人框架为LLM基准测试提供了一个模块化和可适应的解决方案.
  • 结果指导在科学和工业领域可靠的LLM部署的模型选择.
  • 该研究解决了当前LLM评估方法的差距.