Update README.md

README.md

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+[Paper](https://arxiv.org/abs/2403.13315) | [Code](https://github.com/declare-lab/LLM-PuzzleTest/tree/master/PuzzleVQA) | [Dataset](https://huggingface.co/datasets/declare-lab/puzzlevqa)
+
+### About
+
+Large multimodal models extend the impressive capabilities of large language models by integrating multimodal
+understanding abilities. However, it is not clear how they can emulate the general intelligence and reasoning ability of
+humans. As recognizing patterns and abstracting concepts are key to general intelligence, we introduce PuzzleVQA, a
+collection of puzzles based on abstract patterns. With this dataset, we evaluate large multimodal models with abstract
+patterns based on fundamental concepts, including colors, numbers, sizes, and shapes. Through our experiments on
+state-of-the-art large multimodal models, we find that they are not able to generalize well to simple abstract patterns.
+Notably, even GPT-4V cannot solve more than half of the puzzles. To diagnose the reasoning challenges in large
+multimodal models, we progressively guide the models with our ground truth reasoning explanations for visual perception,
+inductive reasoning, and deductive reasoning. Our systematic analysis finds that the main bottlenecks of GPT-4V are
+weaker visual perception and inductive reasoning abilities. Through this work, we hope to shed light on the limitations
+of large multimodal models and how they can better emulate human cognitive processes in the future.
+
+### Example Puzzle
+
+The figure below shows an example question which involves the color concept in PuzzleVQA, and an incorrect answer from
+GPT-4V. There are generally three stages that can be observed in the solving process: visual perception (blue),
+inductive reasoning (green), and deductive reasoning (red). Here, the visual perception was incomplete, causing a
+mistake during deductive reasoning.
+
+![](images/example.png)
+
+### Puzzle Components
+
+The figure below shows an illustration example of components (top) and reasoning explanations (bottom) for abstract
+puzzles in PuzzleVQA. To construct each puzzle instance, we first define the layout and pattern of a multimodal
+template, and populate the
+template with suitable objects that demonstrate the underlying pattern. For interpretability, we also construct ground
+truth reasoning explanations to interpret the puzzle and explain the general solution stages.
+
+![](images/components.png)
+
+### Puzzle Taxonomy
+
+The figure below shows the taxonomy of abstract puzzles in PuzzleVQA with sample questions, based on fundamental
+concepts
+such as colors and size. To enhance diversity, we design both single-concept and dual-concept puzzles.
+
+![](images/taxonomy.png)
+
+### Evaluation Results
+
+We report the main evaluation results on single-concept and dual-concept puzzles in Table 1 and Table 2 respectively.
+The evaluation results for single-concept puzzles, as shown in Table 1 reveal notable differences in performance among
+the open-source and closed-source models. GPT-4V stands out with the highest average score of 46.4, demonstrating
+superior abstract pattern reasoning on single-concept puzzles such as numbers, colors, and size. It particularly excels
+in the "Numbers" category with a score of 67.5, far surpassing other models, which may be due to its advantage in math
+reasoning tasks (Yang et al., 2023). Claude 3 Opus follows with an overall average of 39.4, showing its strength in
+the "Shapes" category with a top score of 44.5. The other models, including Gemini Pro and LLaVA-13B trail behind with
+averages of 34.5 and 27.5 respectively, performing similarly to the random baseline on several categories.
+
+In the evaluation on dual-concept puzzles, as shown in Table 2, GPT-4V stands out again with the highest average score
+of 45.5. It performed particularly well in categories such as "Colors & Numbers" and "Colors & Size" with a score of
+56.0 and 55.0 respectively. Claude 3 Opus closely follows with an average of 43.7, showing strong performance in "
+Numbers & Size" with the highest score of 34.0. Interestingly, LLaVA-13B, despite its lower overall average of 31.1,
+scores the highest in the "Size & Shapes" category at 39.0. Gemini Pro, on the other hand, has a more balanced
+performance across categories but with a slightly lower overall average of 30.1. Overall, we find that models perform
+similarly on average for single-concept and dual-concept patterns, which suggests that they are able to relate multiple
+concepts such as colors and numbers together.
+
+![](images/results.png)
+
+### Citation
+
+If our work inspires your research, please cite us:
+
+```
+@article{chia2024puzzlevqa,
+  title={PuzzleVQA: Diagnosing Multimodal Reasoning Challenges of Language Models with Abstract Visual Patterns},
+  author={Yew Ken Chia and Vernon Toh Yan Han and Deepanway Ghosal and Lidong Bing and Soujanya Poria},
+  journal={arXiv preprint arXiv:2403.13315},
+  year={2024}
+}
+```