Capturing Linguistic Complexity in LLMs: NLP Fundamental Principles and Their Implementation in ChatGPT

Abstract

This comprehensive research synthesizes the foundational principles of Natural Language Processing (NLP) and their realization within ChatGPT, examining the ways deep learning architectures internalize profound linguistic complexity. Structural interplay is investigated. A dual-track empirical framework is systematically employed. By contrasting traditional Long Short-Term Memory (LSTM) networks with the Transformer architecture, the first track effectively demonstrates how parallelized self-attention maintains deep semantic coherence. High-order representational accuracy is achieved. The Qwen2.5-1.5B series is analyzed. By systematically comparing "Base" and "Instruct" models to decouple intelligence origins, the second track reveals that while massive scaling creates an expansive "Cognitive Reservoir" of knowledge, Reinforcement Learning from Human Feedback (RLHF) provides the essential "Functional Bridge" for precise, intent-driven execution. Aligned utility is realized. Ultimately, modern AI is viewed as the synergistic integration of structural efficiency, volumetric growth, and intentional refinement.

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