Accelerating human–computer interaction through convergent conditions for LLM explanation - Raikov, Aleksandr; Giretti, Alberto; Pirani, Massimiliano; Spalazzi, Luca; Guo, Meng (Frontiers in Artificial Intelligence, 2024)
The article addresses the accelerating human-machine interaction using the Large Language Modellarge language model (LLM). It goes beyond the traditional logical paradigms of eXplainable Artificial Intelligenceexplainable artificial intelligence (XAI) by considering non-digital cognitive, poor-formalizable, and non-local semantics cognitive semantical interpretations of LLM. Such semantics take into account quantum and optical effects. 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 modelling, genetic algorithm, neural network, causal loop dynamics, and eigenform realisation. It ishas been shown that decision-makers need to create unique structural conditions for information processes while, using LLM to accelerate the convergence of collective problem-solving to goals. The implementations have been carried out during the collective strategic planning in situational centres. The study is helpful for the advancement of explainable LLM in many branches of economy, science and technology.