Adapting LLMs for Satellite Communications: Methodology, Challenges, and Impact. - Mozo-Quesada, Alejandro Jesús; Gálvez-Rojas, Sergio; Christou, Ioannis; Vogiatzis, Dimitrios; Navarro, Tomás; López-Valverde, Francisco (IEEE, 2025)
The application of large language models (LLMs) to specialized fields, such as Satellite Communications (SatCom), presents unique challenges due to the extensive and cutting-edge knowledge required. SatCom encompasses a wide range of technical details, protocols, and operational guidelines that must be addressed to produce effective and accurate models for practical use. This paper presents a fine-tuning approach for adapting 7-billion-parameter instructed LLMs (Llama-3v and Mistral) to SatCom, using a proprietary corpus sourced from the European Space Agency (ESA) consisting of domain-specific PDF documents. The confidential nature of this corpus imposes constraints on both model training and evaluation, demanding a sensible text extraction pipeline capable of handling complex structures, such as tables, to preserve critical information. Our fine-tuning methodology employs a carefully configured process, followed by an automatic evaluation framework using a curated Q&A set tailored to SatCom. Models were created in both non-quantified and 8-bit quantized formats, ensuring feasibility for desktop-level inference. The fine-tuned models demonstrated a 6,6% improvement over the baseline LLM, as well as significant gains when compared to retrieval-augmented generation (RAG) methods. These results indicate a promising advancement in the development of LLMs for domain-specific applications within the SatCom field.
Keywords: Inteligencia artificial; Aprendizaje automático (Inteligencia artificial); Comunicaciones vía satélite; Proceso en lenguaje natural (Informática); Artificial intelligence; Evaluation models; Large language models; Fine tuning LLMs; Preprocessing for LLMs; Satellite communications