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Review
. 2024 May 30:7:1406773.
doi: 10.3389/frai.2024.1406773. eCollection 2024.

Accelerating human-computer interaction through convergent conditions for LLM explanation

Aleksandr Raikov  1 , Alberto Giretti  2 , Massimiliano Pirani  3 , Luca Spalazzi  3 , Meng Guo  1
Affiliations
Review

Accelerating human-computer interaction through convergent conditions for LLM explanation

Aleksandr Raikov et al. Front Artif Intell. .

Abstract

The article addresses the accelerating human-machine interaction using the large language model (LLM). It goes beyond the traditional logical paradigms of explainable artificial intelligence (XAI) by considering poor-formalizable cognitive semantical interpretations of LLM. XAI is immersed in a hybrid space, where humans and machines have crucial distinctions during the digitisation of the interaction process. The author's convergent methodology ensures the conditions for making XAI purposeful and sustainable. This methodology is based on the inverse problem-solving method, cognitive modeling, genetic algorithm, neural network, causal loop dynamics, and eigenform realization. It has been shown that decision-makers need to create unique structural conditions for information processes, using LLM to accelerate the convergence of collective problem solving. The implementations have been carried out during the collective strategic planning in situational centers. The study is helpful for the advancement of explainable LLM in many branches of economy, science and technology.

Keywords: LLM; causal loop dynamics; cognitive semantics; cybernetics; eigenforms; explainable artificial intelligence; hybrid reality; socio-economic environment.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Causal loop diagram produced by humans in modeling the complex dynamics generated by AI. Red elements concern the non-formalizable part of the CLD.
Figure 2
Figure 2
Causal loop diagram produced by machines (AI) in modeling complex dynamics generated by human behavior.
Figure 3
Figure 3
Stock and flow diagram that can constitute explicit dynamics and simulator for the AI starting from the CLD of Figure 2. The red elements concern the non-formalizable part of the system, while the blue elements concern the actuations that the AI can enact on the system.
Figure 4
Figure 4
Explanation created by cognitive modeling.
See this image and copyright information in PMC

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