{"ID":2883303,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.09240","arxiv_id":"2508.09240","title":"NEFMind: Parameter-Efficient Fine-Tuning of Open-Source LLMs for Telecom APIs Automation","abstract":"The use of Service-Based Architecture in modern telecommunications has exponentially increased Network Functions (NFs) and Application Programming Interfaces (APIs), creating substantial operational complexities in service discovery and management. We introduce \\textit{NEFMind}, a framework leveraging parameter-efficient fine-tuning of open-source Large Language Models (LLMs) to address these challenges. It integrates three core components: synthetic dataset generation from Network Exposure Function (NEF) API specifications, model optimization through Quantized-Low-Rank Adaptation, and performance evaluation via GPT-4 Ref Score and BertScore metrics. Targeting 5G Service-Based Architecture APIs, our approach achieves 85% reduction in communication overhead compared to manual discovery methods. Experimental validation using the open-source Phi-2 model demonstrates exceptional API call identification performance at 98-100% accuracy. The fine-tuned Phi-2 model delivers performance comparable to significantly larger models like GPT-4 while maintaining computational efficiency for telecommunications infrastructure deployment. These findings validate domain-specific, parameter-efficient LLM strategies for managing complex API ecosystems in next-generation telecommunications networks.","short_abstract":"The use of Service-Based Architecture in modern telecommunications has exponentially increased Network Functions (NFs) and Application Programming Interfaces (APIs), creating substantial operational complexities in service discovery and management. We introduce \\textit{NEFMind}, a framework leveraging parameter-efficie...","url_abs":"https://arxiv.org/abs/2508.09240","url_pdf":"https://arxiv.org/pdf/2508.09240v1","authors":"[\"Zainab Khan\",\"Ahmed Hussain\",\"Mukesh Thakur\",\"Arto Hellas\",\"Panos Papadimitratos\"]","published":"2025-08-12T15:03:22Z","proceeding":"cs.NI","tasks":"[\"cs.NI\",\"cs.AI\",\"cs.CL\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}