Rebus Solving Capability

Rebus solving, the ability of artificial intelligence to decipher visual and textual puzzles requiring multi-step reasoning, serves as a rigorous benchmark for evaluating multimodal large language models' cognitive abilities. Current research focuses on assessing the performance of models like GPT-4 and LLaMA on diverse rebus datasets, revealing significant limitations in their ability to combine image recognition, linguistic understanding, and complex reasoning, even with fine-tuning. These findings highlight critical gaps in current AI capabilities, particularly in areas like symbolic manipulation and common-sense reasoning, and provide valuable insights for improving model design and training methodologies.