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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.
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Needs Companion: A Novel Approach to Continuous User Needs Sensing Using Virtual Agents and Large Language Models.

Takuya Nakata1, Masahide Nakamura2,3, Sinan Chen2

  • 1Graduate School of Engineering, Kobe University, 1-1 Rokkodai-cho, Nada-ku, Kobe 657-8501, Hyogo, Japan.

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Summary
This summary is machine-generated.

This study introduces Needs Companion, a system that automatically detects individual service needs using virtual agents and large language models (LLMs). It enables accurate needs sensing for personalized services.

Keywords:
human-centered designlarge language modelneedpersonalizationservicevirtual agent

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Area of Science:

  • Human-Computer Interaction
  • Artificial Intelligence
  • Service Science

Background:

  • Services are crucial for daily life, necessitating personalized approaches.
  • Current methods often infer needs from behavior logs, lacking direct detection.
  • Creating a human-centered society requires understanding individual service requirements.

Purpose of the Study:

  • Introduce Needs Companion for automatic individual service need detection.
  • Establish a foundation for accurate needs sensing.
  • Provide interpretable data for personalized service development.

Main Methods:

  • Developed a needs data model based on the 6W1H framework.
  • Employed virtual agents for needs elicitation.
  • Utilized large language models (LLMs) for automated needs analysis and extraction.

Main Results:

  • The Needs Companion system demonstrated accurate and rapid detection of individual service needs.
  • Experimental results validate the system's effectiveness in identifying user requirements.
  • Generated interpretable data crucial for tailoring services.

Conclusions:

  • Needs Companion advances automated needs detection for personalized services.
  • The research contributes to machine learning, human-centered design, and requirements engineering.
  • This work paves the way for more responsive and user-centric service systems.