Argumentative Stance Prediction: An Exploratory Study on Multimodality
  and Few-Shot Learning
Arushi Sharma Abhibha Gupta Maneesh Bilalpur
To advance argumentative stance prediction as a multimodal problem, the First
Shared Task in Multimodal Argument Mining hosted stance prediction in crucial
social topics of gun control and abortion. Our exploratory study attempts to
evaluate the necessity of images for stance prediction in tweets and compare
out-of-the-box text-based large-language models (LLM) in few-shot settings
against fine-tuned unimodal and multimodal models. Our work suggests an
ensemble of fine-tuned text-based language models (0.817 F1-score) outperforms
both the multimodal (0.677 F1-score) and text-based few-shot prediction using a
recent state-of-the-art LLM (0.550 F1-score). In addition to the differences in
performance, our findings suggest that the multimodal models tend to perform
better when image content is summarized as natural language over their native
pixel structure and, using in-context examples improves few-shot performance of
LLMs.