Exploring Large Language Models in Intent Acquisition and Translation

Intent-based networking has attracted interest in the academic research for enhancing network management operations with user-oriented features. One of the main challenge in this field is the acquisition of the user intents and subsequently the relative translation into policies for the automatic management of the network. Concerning this task, the primary technique employed is relying on Graphical User Interfaces (GUI)s. In addition, the use of Natural Language Processing techniques has been extensively adopted for improved user experience. Recently, some preliminary studies have shown that using Large Language Models (LLMs) for this purpose leads to achieve interesting results. However, based on a comprehensive analysis of the state of the art, it has emerged that the works utilizing the LLMs do not fully exploit all the capabilities these tools could potentially offer. For this reason, the doctoral work aims to address the following challenges: enhancing user experience through the utilization of intelligent chatbots, improving the correct understanding of user intents and ensuring the translation of user intentions into a coherent set of network configurations, which are generated automatically.