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Accelerating human-computer interaction through convergent conditions for LLM explanation.

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Summary

This study enhances human-machine interaction using large language models (LLMs) and explainable artificial intelligence (XAI). A novel convergent methodology accelerates collective problem-solving for decision-makers in strategic planning.

Keywords:
LLMcausal loop dynamicscognitive semanticscyberneticseigenformsexplainable artificial intelligencehybrid realitysocio-economic environment

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Area of Science:

  • Artificial Intelligence
  • Cognitive Science
  • Human-Computer Interaction

Background:

  • Traditional explainable artificial intelligence (XAI) paradigms struggle with the nuances of large language models (LLMs).
  • Human-machine interaction is rapidly evolving, necessitating advanced AI interpretability.
  • LLMs introduce complex cognitive and semantic elements into AI systems.

Purpose of the Study:

  • To develop a methodology for accelerating human-machine interaction using LLMs.
  • To extend XAI beyond traditional logical frameworks by incorporating cognitive interpretations.
  • To ensure purposeful and sustainable XAI in hybrid human-machine environments.

Main Methods:

  • A convergent methodology integrating inverse problem-solving, cognitive modeling, genetic algorithms, neural networks, causal loop dynamics, and eigenform realization.
  • Analysis of LLM's poor-formalizable cognitive-semantic interpretations.
  • Implementation in collective strategic planning scenarios within situational centers.

Main Results:

  • Demonstrated acceleration of collective problem-solving convergence through LLM integration.
  • Established conditions for purposeful and sustainable XAI in digitized human-machine interactions.
  • Successful application in real-world strategic planning environments.

Conclusions:

  • Decision-makers can leverage LLMs to create unique structural conditions for information processes.
  • The proposed methodology enhances the effectiveness of XAI for complex problem-solving.
  • Findings support the advancement of explainable LLMs across various economic, scientific, and technological sectors.