Accelerating human-computer interaction through convergent conditions for LLM explanation

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.