Single-label multi-modal field of research classification

Abstract

The automated field of research classification for scientific papers is still challenging, even with modern tools such as large language models. As part of a shared task tackling this problem, this paper presents our contribution SLAMFORC, an approach to single-label classification using multi-modal data. We combined the metadata of papers with their full text and, where available, images into a pipeline to predict their field of research with an ensemble voting on traditional classifiers and large language models. We evaluated our approach on the shared task dataset and scored the highest values for two of the four metrics used in the evaluation of the competition, with the other two being the second highest.