Using generative AI to support standardization work -- the case of 3GPP
This addresses the effort-intensive and costly task of consensus-building in large standardization organizations, representing an incremental improvement by applying existing AI methods to a new domain.
The paper tackled the problem of identifying similarities, dissimilarities, and discussion points in standardization processes like 3GPP to make them more cost-efficient, faster, and reliable, using large language models; results showed generic models correlate well with expert assessments (Pearson correlation 0.66-0.98) but domain-specific models are needed for better discussion materials.
Standardization processes build upon consensus between partners, which depends on their ability to identify points of disagreement and resolving them. Large standardization organizations, like the 3GPP or ISO, rely on leaders of work packages who can correctly, and efficiently, identify disagreements, discuss them and reach a consensus. This task, however, is effort-, labor-intensive and costly. In this paper, we address the problem of identifying similarities, dissimilarities and discussion points using large language models. In a design science research study, we work with one of the organizations which leads several workgroups in the 3GPP standard. Our goal is to understand how well the language models can support the standardization process in becoming more cost-efficient, faster and more reliable. Our results show that generic models for text summarization correlate well with domain expert's and delegate's assessments (Pearson correlation between 0.66 and 0.98), but that there is a need for domain-specific models to provide better discussion materials for the standardization groups.