Adapting foundation models for information synthesis of wireless communication specifications

Abstract

Existing approaches to understanding, developing, and researching modern wireless communication technologies involve time intensive and arduous processes of sifting through numerous webpages and technical specification documents, gathering the required information and synthesizing it. The present disclosure describes a conversational artificial intelligence tool for information synthesis of wireless communication specifications. The system builds on recent advancements in foundation large language models (LLMs) and consists of three key additional components: a domain-specific database, a context extractor, and a feedback mechanism. The system appends user queries with concise contextual information extracted from a database of wireless technical specifications and incorporates tools for expert feedback and data contribution. On evaluation using a benchmark dataset of expert queries and responses, the system provided more relevant and accurate answers on topics related to modern wireless communication specifications with a BLEU (BiLingual Evaluation Understudy) score of 0.28 compared to 0.03 achieved by current state-of-the-art LLM-based systems.