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.

通过对推文中的立场预测的图像的必要性进行了评估，并使用了现有的大型语言模型进行了对比，研究表明，精调的基于文本的语言模型的集合优于多模态模型和使用最新的大型语言模型进行的基于文本的少量训练数据的预测。此外，多模态模型在将图像内容摘要为自然语言时表现更好，使用上下文示例可以改善大规模语言模型的少量训练数据的性能。