Advancing Natural Language Processing in Telecommunications: Models, Benchmarks, and Deployment Challenges

Natural Language Processing (NLP) has emerged as an enabler for automation and intelligence for the telecom industry, driving applications such as customer sentiment analysis, network management, and technical document processing.This systematic review examines 20 peer-reviewed studies between 2020 and 2025 across three key use cases: customer experience improvement (35%), technical document mining (30%), and network management automation (25%).Domain-specific language models like Tele-LLMs and retrieval-augmented generation (RAG) models consistently beat general-purpose models like GPT-4, reaching up to 23% higher telecomspecific benchmark accuracy.Edge deployment breakthroughs like pruning, quantization, and distillation facilitate up to 4 reduced latency for real-time inference, maintaining up to 95% of the original performance of the model.Challenges that still exist include scarcities of data, multilingual support, integration with legacy systems, and concepts that drift through the fast pace of standards development.This review outlines current capabilities, describes the gap between current research and published papers in Dongyu et al., and outlines some potential future directions such as federated learning, multimodal model design, and hybrid edge-cloud deployment to enable NLP applications to advance to next-generation telecommunications networks.