LLMs Struggle with Abstract Meaning Comprehension More Than Expected

This research investigates LLMs' ability to comprehend abstract meanings, revealing that models like GPT-4o struggle in zero-shot, one-shot, and few-shot settings, while fine-tuned models like BERT and RoBERTa perform better. It proposes a bidirectional attention classifier that significantly enhances the accuracy of fine-tuned models in interpreting abstract concepts.