Treffer: Thinking GPT Inspired by Descartes' 'Discourse on the Method'

The rapid advancement of Generative Pre-trained Transformer (GPT) models has revolutionized natural language processing, allowing machines to produce highly fluent and contextually relevant text. However, these models often exhibit limitations in deep reasoning, context comprehension, and ethical considerations, which can result in linguistically coherent but semantically shallow responses. This paper introduces an enhanced framework inspired by René Descartes' Discourse on the Method, focusing on integrating structured thinking principles, interdisciplinary awareness, and ethical alignment within GPT models. Unlike previous prompt-engineering-based methods, the proposed framework leverages fine-tuning techniques to embed Descartes' principles of reasoning, metacognition, contextual awareness, and moral considerations directly into the model's architecture. We implemented this framework using Python and Huggingface's GPT-2 and applied a role-based modular refinement approach during the training process. This method incorporates roles such as Critic, Analyzer, Ethicist, and Verifier, each embodying specific aspects of Descartes' method to iteratively refine the model's output. Experimental results indicate that the fine-tuned model exhibits notable improvements in reasoning depth, ethical alignment, and response quality across various task categories. The architecture further demonstrates scalability and adaptability by integrating dynamic role assignment and memory support, allowing the model to enhance its responses based on context and prior knowledge. Empirical evaluations and qualitative analysis reveal that our framework reduces bias, improves logical coherence, and aligns model outputs with ethical standards. This research paves the way for the development of AI systems capable of more human-like reasoning and adaptable to complex, interdisciplinary scenarios.