OPTIMIZATION AND FUTURE TRENDS OF LLM-BASED INTELLIGENT CUSTOMER SERVICE SYSTEMS

Abstract

The article provides a comprehensive analysis of the problems and prospects for optimizing Intelligent Customer Service Systems in the context of rapid business process digitalization. It examines the current challenges facing dialogue systems using the Alibaba Xiaomi platform as an example, particularly limitations in semantic context interpretation, processing unstructured "long-tail" queries, and maintaining the naturalness of communication. The impact of these factors on key performance indicators, such as response time, response accuracy, and user satisfaction level, is analyzed. The paper proposes and substantiates a three-level strategy for improving the architecture of intelligent customer service using the advanced capabilities of Large Language Models (LLMs). The first level involves optimizing context management mechanisms based on the Transformer architecture. The second level focuses on implementing fully-fledged multimodal interaction. It describes approaches to integrating heterogeneous data streams (voice, image, text) into a single vector space using models like CLIP, which ensures the synergistic processing of visual and verbal queries in real time. The third level of modernization is dedicated to developing an adaptive sentiment analysis module. It considers the application of deep learning algorithms (in particular, the fine-tuning of RoBERTa models) to recognize complex emotional constructs such as sarcasm, irony, or hidden dissatisfaction. A mechanism for dynamically adjusting the tone of responses and problem-solving strategies depending on the client's emotional state is proposed. Special attention in the article is paid to future industry development trends, in particular, the deep integration of generative models with Knowledge Graphs. It is substantiated that such a combination will improve the factual accuracy of responses, provide the capability for logical inference, and minimize the risks of neural network "hallucinations" in critical domains such as medicine, finance, and education. Based on the conducted modeling and analysis of predictive data (2022–2025), it is proven that the implementation of the proposed solutions can increase the accuracy of contextual understanding and the overall level of user satisfaction by 2-6%, which confirms the practical value of the study for the further development of digital service ecosystems