Research and Analysis on Text Interaction Methods Based on Large Language Models

Large Language Models (LLMs), as a cutting-edge technology in the field of artificial intelligence, have revolutionized human-computer interaction paradigms by integrating natural language processing, Transformer architecture, and reinforcement learning techniques, thereby achieving in-depth understanding and generation of linguistic logic and emotions. Their technological core lies in the Transformer architecture trained on massive datasets, which can capture complex semantics and contextual associations, optimize generation quality through reinforcement learning, and enhance interpersonal-like interaction via affective computing. This paper systematically reviews the technical framework and application scenarios of LLMs: in fields such as intelligent customer service, educational assessment, and game narration, these models demonstrate application values such as multi-turn dialogue, personalized learning path planning, and dynamic plot generation. Meanwhile, it delves into the challenges faced by technological development, including ethical risks arising from biases in training data, deployment cost issues due to high computational power requirements, and deficiencies in the controllability of generated content. In response to these issues, collaborative solutions such as multimodal data fusion, lightweight model deployment, and interdisciplinary ethical governance are proposed. Research indicates that LLMs are at a critical stage of transitioning from technological breakthroughs to large-scale applications, and their sustainable development necessitates the construction of a technological governance framework to achieve social value balance, on top of algorithm optimization and computational power enhancement. Future research should focus on enhancing model interpretability, exploring green computing pathways, and promoting the virtuous development of technology through human-machine collaboration mechanisms.