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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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Related Experiment Video

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

681

A scalable framework for evaluating multiple language models through cross-domain generation and hallucination

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
Summary

A new benchmarking framework, MultiLLM-Chatbot, evaluated large language models (LLMs) in specialized domains. LLAMA-3.3-70B demonstrated superior performance across Agriculture, Biology, Economics, IoT, and Medical fields.

Keywords:
Bias DetectionElasticsearchHallucination DetectionLarge Language Models (LLMs)Multi-Domain BenchmarkingRetrieval-Augmented Generation (RAG)Semantic Similarity

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Area of Science:

  • Artificial Intelligence
  • Natural Language Processing
  • Information Retrieval

Background:

  • Large language models (LLMs) have advanced retrieval-augmented generation (RAG) systems.
  • Challenges like semantic similarity, bias, and hallucinations persist in domain-specific LLM applications.

Purpose of the Study:

  • Introduce MultiLLM-Chatbot, a scalable RAG-based benchmarking framework.
  • Evaluate five popular LLMs (GPT-4-Turbo, CLAUDE-3.7-Sonnet, LLAMA-3.3-70B, DeepSeek-R1-Zero, Gemini-2.0-Flash).
  • Assess LLM performance across five domains: Agriculture, Biology, Economics, Internet of Things (IoT), and Medical.

Main Methods:

  • Generated 250 standardized queries from 50 peer-reviewed papers.
  • Extracted and segmented PDF texts, embedded them, and indexed in Elasticsearch.
  • Analyzed 1,250 model responses using cosine similarity, VADER sentiment analysis, TF-IDF, and Named Entity Recognition (NER).

Main Results:

  • LLAMA-3.3-70B emerged as the top-performing model overall.
  • LLAMA-3.3-70B led in all five evaluated domains.
  • The framework provides a reproducible pipeline for domain-specific LLM benchmarking.

Conclusions:

  • The MultiLLM-Chatbot framework offers a modular and adaptable solution for LLM benchmarking.
  • Findings guide model selection for trustworthy LLM deployment in scientific and industrial sectors.
  • The study addresses gaps in current LLM evaluation methodologies.