PuzzleVQA: Diagnosing Multimodal Reasoning Challenges of Language Models with Abstract Visual Patterns

• URL: http://arxiv.org/abs/2403.13315v3
• Date: Sat, 17 Aug 2024 11:56:08 GMT
• Title: PuzzleVQA: Diagnosing Multimodal Reasoning Challenges of Language Models with Abstract Visual Patterns
• Authors: Yew Ken Chia, Vernon Toh Yan Han, Deepanway Ghosal, Lidong Bing, Soujanya Poria,
• Abstract summary: We evaluate large multimodal models with abstract patterns based on fundamental concepts. We find that they are not able to generalize well to simple abstract patterns. Our systematic analysis finds that the main bottlenecks of GPT-4V are weaker visual perception and inductive reasoning abilities.
• Score: 69.17409440805498
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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 2000 puzzle instances 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, GPT-4V achieves a score of 46.4% on single-concept puzzles, which shows that state-of-the-art models struggle on our dataset. 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. Our data and code are available at https://puzzlevqa.github.io

Related papers

• The Jumping Reasoning Curve? Tracking the Evolution of Reasoning Performance in GPT-[n] and o-[n] Models on Multimodal Puzzles [29.214813685163218]
  OpenAI's releases of o1 and o3 mark a paradigm shift in Large Language Models towards advanced reasoning capabilities. We track the evolution of the GPT-[n] and o-[n] series models on challenging multimodal puzzles. The superior performance of o1 comes at nearly 750 times the computational cost of GPT-4o, raising concerns about its efficiency.
  arXiv  Detail & Related papers  (2025-02-03T05:47:04Z)
• DRIVINGVQA: Analyzing Visual Chain-of-Thought Reasoning of Vision Language Models in Real-World Scenarios with Driving Theory Tests [69.00444996464662]
  We present DrivingVQA, a new benchmark derived from driving theory tests to evaluate visual chain-of-thought reasoning in complex real-world scenarios. Our experiments reveal that open-source and proprietary LVLMs struggle with visual chain-of-thought reasoning under zero-shot settings. We investigate training strategies that leverage relevant entities to improve visual reasoning.
  arXiv  Detail & Related papers  (2025-01-08T18:31:16Z)
• Visual-O1: Understanding Ambiguous Instructions via Multi-modal Multi-turn Chain-of-thoughts Reasoning [53.45295657891099]
  This paper proposes Visual-O1, a multi-modal multi-turn chain-of-thought reasoning framework. It simulates human multi-modal multi-turn reasoning, providing instantial experience for highly intelligent models. Our work highlights the potential of artificial intelligence to work like humans in real-world scenarios with uncertainty and ambiguity.
  arXiv  Detail & Related papers  (2024-10-04T11:18:41Z)
• Whiteboard-of-Thought: Thinking Step-by-Step Across Modalities [30.96613796974929]
  We introduce a simple method to unlock the visual reasoning capabilities of multimodal large language models. Whiteboard-of-thought prompting provides models with a metaphorical whiteboard' to draw out reasoning steps as images. This simple approach shows state-of-the-art results on four difficult natural language tasks.
  arXiv  Detail & Related papers  (2024-06-20T17:59:45Z)
• REBUS: A Robust Evaluation Benchmark of Understanding Symbols [1.90463290938268]
  GPT-4o significantly outperforms all other models, followed by proprietary models outperforming all other evaluated models. Even the best model has a final accuracy of only 42%, which goes down to just 7% on hard puzzles. Our benchmark can therefore be used to identify major shortcomings in the knowledge and reasoning of multimodal large language models.
  arXiv  Detail & Related papers  (2024-01-11T00:30:28Z)
• BRAINTEASER: Lateral Thinking Puzzles for Large Language Models [15.95314613982879]
  BRAINTEASER is a multiple-choice Question Answering task designed to test the model's ability to exhibit lateral thinking. Our experiments with state-of-the-art instruction- and commonsense language models reveal a significant gap between human and model performance. We make all of our code and data available to stimulate work on developing and evaluating lateral thinking models.
  arXiv  Detail & Related papers  (2023-10-08T07:46:01Z)
• Visual Chain of Thought: Bridging Logical Gaps with Multimodal Infillings [61.04460792203266]
  We introduce VCoT, a novel method that leverages chain-of-thought prompting with vision-language grounding to bridge the logical gaps within sequential data. Our method uses visual guidance to generate synthetic multimodal infillings that add consistent and novel information to reduce the logical gaps for downstream tasks.
  arXiv  Detail & Related papers  (2023-05-03T17:58:29Z)
• Does Deep Learning Learn to Abstract? A Systematic Probing Framework [69.2366890742283]
  Abstraction is a desirable capability for deep learning models, which means to induce abstract concepts from concrete instances and flexibly apply them beyond the learning context. We introduce a systematic probing framework to explore the abstraction capability of deep learning models from a transferability perspective.
  arXiv  Detail & Related papers  (2023-02-23T12:50:02Z)
• MultiViz: An Analysis Benchmark for Visualizing and Understanding Multimodal Models [103.9987158554515]
  MultiViz is a method for analyzing the behavior of multimodal models by scaffolding the problem of interpretability into 4 stages. We show that the complementary stages in MultiViz together enable users to simulate model predictions, assign interpretable concepts to features, perform error analysis on model misclassifications, and use insights from error analysis to debug models.
  arXiv  Detail & Related papers  (2022-06-30T18:42:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.