A Mental Reasoning Driven Large Language Model for Smarter Human AI-Interaction

Abstract

The Large Language Model (LLM) is a sophisticated artificial intelligence system designed to think and react in ways very close to how humans do in their minds. Its primary focus is on understanding users' intentions, breaking complex questions into simpler parts, and delivering accurate and insightful responses in a concise format. To improve its performance over time, it uses reasoning, memory, and learning skills. The Mental LLM is designed to deliver quick, accurate, and secure results across a range of fields, including data analysis, customer service, education, and health care support. It improves communication and problem-solving skills by mimicking human-like cognitive processes, including logical decision-making, memory, and step-by-step thinking. The increasing reliance on AI systems for routine tasks requires not only accurate but also "contextual" responses closer to human reasoning. To improve human-AI interaction, this project proposes a Mental Reasoning-Driven Large Language Model (LLM) that integrates cognitive reasoning into conventional language models. The system can interpret users' intentions, understand context, and deliver accurate responses through a combination of symbolic reasoning, neural networks, and knowledge integration. Applications in the fields of education, healthcare, customer support, and decision-making can benefit from the model's capability to adapt to user interactions, maintain multi-turn dialogues, and provide personalised interactions. This method bridges conventional AI responses with human-like comprehension. Large Language Models (LLMs) have changed the dynamics of human-AI interactions. The practicality of LLMs in real-world scenarios is limited by their inability to perform deep reasoning and learning. To create a model that mimics human thought processes, a Mental Reasoning-Driven Large Language Model is proposed.