REBUS: A Robust Evaluation Benchmark of Understanding Symbols
We propose a new benchmark evaluating the performance of multimodal large language models on rebus puzzles. The dataset covers 333 original examples of image-based wordplay, cluing 13 categories such as movies, composers, major cities, and food. To achieve good performance on the benchmark of identifying the clued word or phrase, models must combine image recognition and string manipulation with hypothesis testing, multi-step reasoning, and an understanding of human cognition, making for a complex, multimodal evaluation of capabilities. We find that proprietary models such as GPT-4V and Gemini Pro significantly outperform all other tested models. However, even the best model has a final accuracy of just 24%, highlighting the need for substantial improvements in reasoning. Further, models rarely understand all parts of a puzzle, and are almost always incapable of retroactively explaining the correct answer. Our benchmark can therefore be used to identify major shortcomings in the knowledge and reasoning of multimodal large language models.
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Tasks
Datasets
REBUS
| Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Benchmark |
|---|---|---|---|---|---|---|
| Multimodal Reasoning | REBUS | GPT-4V | Accuracy | 24.0 | # 1 | |
| Multimodal Reasoning | REBUS | Gemini Pro | Accuracy | 13.2 | # 2 | |
| Multimodal Reasoning | REBUS | LLaVa-1.5-13B | Accuracy | 1.8 | # 3 | |
| Multimodal Reasoning | REBUS | LLaVa-1.5-7B | Accuracy | 1.5 | # 4 | |
| Multimodal Reasoning | REBUS | BLIP2-FLAN-T5-XXL | Accuracy | 0.9 | # 5 | |
| Multimodal Reasoning | REBUS | CogVLM | Accuracy | 0.9 | # 5 | |
| Multimodal Reasoning | REBUS | QWEN | Accuracy | 0.9 | # 5 | |
| Multimodal Reasoning | REBUS | InstructBLIP | Accuracy | 0.6 | # 8 |
