Adapting Foundation Models for Information Synthesis of Wireless Communication Specifications

TL;DR

This paper introduces NextGen Communications Copilot, an AI tool that synthesizes wireless communication specifications more accurately than existing models by integrating domain-specific data and expert feedback.

Contribution

It presents a novel AI system that enhances foundation models with specialized data and feedback mechanisms for better technical information synthesis.

Findings

Outperforms state-of-the-art tools in BLEU and BERTScore metrics.

Demonstrates improved relevance and accuracy in technical query responses.

Utilizes a domain-specific database and context extraction for tailored answers.

Abstract

Existing approaches to understanding, developing and researching modern wireless communication technologies involves time-intensive and arduous process of sifting through numerous webpages and technical specification documents, gathering the required information and synthesizing it. This paper presents NextGen Communications Copilot, a conversational artificial intelligence tool for information synthesis of wireless communication specifications. The system builds on top of recent advancements in foundation models 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 and query-dependent contextual information extracted from a database of wireless technical specifications and incorporates tools for expert feedback and data contributions. On evaluation using a benchmark dataset of queries and reference responses created by subject matter experts, the system demonstrated more relevant and accurate answers with an average BLEU score and BERTScore F1-measure of 0.37 and 0.79 respectively compared to the corresponding values of 0.07 and 0.59 achieved by state-of-the-art tools like ChatGPT.