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

Yew Ken Chia, Vernon Toh Yan Han, Deepanway Ghosal, Lidong Bing, Soujanya Poria

arXiv:2403.13315v3cs.CV

TL;DR

The paper addresses uncertainty about whether large multimodal models can emulate human-like reasoning, using PUZZLEVQA’s abstract-pattern puzzles to evaluate and diagnose that ability. The experiments find that models struggle to generalize to these patterns, and the analysis identifies weak visual perception and inductive reasoning as GPT-4V bottlenecks.

  • Problem

    It remains unclear how large multimodal models emulate human-like general intelligence and reasoning, including recognizing patterns and abstracting concepts.

    Source: §1 · pp. 1–2

  • Method

    The authors introduce PUZZLEVQA, an automatically generated dataset of abstract-pattern puzzles with ground-truth explanations, and progressively provide those explanations to diagnose reasoning stages.

    Source: §1 · p. 2, §3.3 · p. 4

  • Results

    Models struggle to generalize to simple abstract patterns; GPT-4V scores 46.4% on single-concept puzzles.

    Source: §1 · p. 2

  • Takeaways & Limitations

    For GPT-4V, the main diagnosed bottlenecks are visual perception and inductive reasoning; other models require more guidance with ground-truth explanations.

    Source: §7 · p. 9

    The study mainly focuses on zero-shot evaluation; few-shot prompting showed inconsistent benefits.

    Source: Limitations · p. 10

Abstract

from arXiv · show

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

1 Introduction

The paper uses abstract patterns to examine whether large multimodal models can emulate human-like cognitive reasoning. It introduces PUZZLEVQA and finds that models struggle with these patterns, with GPT-4V’s main bottlenecks in visual perception and inductive reasoning.

1 Introduction

  • The authors use abstract patterns to investigate how large multimodal models might emulate human general intelligence and reasoning.They frame the open question as whether models can perceive and interpret information, generalize from observations, and apply principles to specific problems.Source: §1 · pp. 1–2
  • The puzzles target fundamental concepts—numbers, colors, shapes, and size—and focus on generalizing to novel problems without relying on extensive world knowledge.The paper presents abstract patterns as a testbed for reasoning processes that include perception, induction, and deduction.Source: §1 · p. 2
  • 46.4% is GPT-4V’s score on single-concept puzzles; the authors identify weaker visual perception and inductive reasoning as its main bottlenecks.The paper also reports that existing models do not generalize well to simple abstract patterns.Source: §1 · p. 2
  • PUZZLEVQA is an automatically generated, diverse collection of 2,000 multimodal puzzle samples with reasoning explanations.The dataset is designed to investigate multimodal models’ cognitive and reasoning abilities using abstract patterns.Source: §1 · p. 2

2 Background: Cognitive Theories

The background frames abstract-pattern puzzles as a way to study novel problem solving and to organize reasoning into stages, while distinguishing fluid from crystallized intelligence.

2.1 Fluid and Crystallized Intelligence

  • Fluid intelligence concerns solving novel problems without previously acquired knowledge, whereas crystallized intelligence draws on accumulated knowledge, skills, and experience.The paper focuses on how models may emulate fluid intelligence through abstract patterns.Source: §2.1 · p. 2

2.2 Cognitive Development

  • The paper maps cognitive development onto puzzle reasoning: perception of basic concepts, inductive generalization from examples, and deduction of a specific solution.The discussion relates these abilities to sensorimotor, concrete operational, and formal operational stages.Source: §2.2 · p. 3

2 Background: Cognitive Theories

  • Abstract-pattern puzzles are used to study how models perceive multimodal information, infer general principles, and apply them to specific cases.The paper adopts this framework as an analogy for cognitive stages, while noting that models do not develop through human-like experience.Source: §2.2 · p. 3

3 PuzzleVQA Dataset

PUZZLEVQA is designed to diagnose multimodal reasoning using automatically generated abstract-pattern puzzles, with staged explanations and controlled response options.

3 PuzzleVQA Dataset

  • PUZZLEVQA targets how multimodal models interpret inputs, generalize from observations, and apply principles, using diverse abstract-pattern puzzles with reasoning explanations.The dataset is automatically constructed through multimodal templates to support interpretability.Source: §3 · pp. 3–4

3.1 Puzzle Components

  • Each puzzle combines objects, a spatial layout, a governing pattern, demonstrations, and a natural-language query asking for the missing object.Demonstrations make the underlying pattern less ambiguous.Source: §3.1 · p. 4

3.2 Design Considerations

  • The design aims for simple patterns, automatic rule-based generation to avoid annotation mistakes, and diversity across four concepts and single- or dual-concept puzzles.Puzzles are not deliberately made more complex than necessary and do not require extensive world knowledge.Source: §3.2 · p. 4

3.3 Puzzle Construction

  • Templates first specify a layout and abstract pattern, then populate them with objects that demonstrate the pattern; explanations describe perception, induction, and deduction.The explanations can be progressively provided to identify reasoning bottlenecks.Source: §3.3 · p. 4

3.4 Multiple-Choice Format

  • Questions use multiple-choice answers to manage possible ambiguity in colors or sizes, with three options for size and four for other questions.Incorrect options are generated with heuristics, and evaluation uses accuracy.Source: §3.4 · pp. 4–5

3.5 Dataset Analysis

  • The taxonomy includes 10 categories, with four single-concept categories and dual-concept patterns that require relating two concepts; 2,000 instances are generated in total.Each template contributes 100 instances, and the authors report that this size is relatively robust to experimental variance.Source: §3.5 · p. 5

3.6 Implementation Details

  • Python and Pillow automate varied puzzle images and questions; each instance includes an image, question, caption, pattern explanation, deduction, options, and answer.Source: §3.6 · pp. 5–6

4 Experimental Setup

The inference pipeline elicits step-by-step multimodal reasoning and extracts a multiple-choice answer when needed. Experiments evaluate five open- and closed-source models.

4.1 Inference Pipeline

  • Zero-shot chain-of-thought prompting asks models to describe the image and think step by step; if no answer letter is extracted, a second prompt extracts the final answer.The main setting supplies only the questions and images; ground-truth explanations are added separately for bottleneck analysis.Source: §4.1 · p. 6

4.2 Models

  • The evaluation includes Qwen-VL-Chat, LLaVA-13B, Gemini Pro, Claude 3 Opus, and GPT-4V.The study uses the open-weight Qwen-VL-Chat and LLaVA-13B models and publicly available APIs for Gemini Pro, Claude 3 Opus, and GPT-4V.Source: §4.2 · pp. 6–7

5 Results

Models score modestly on both single- and dual-concept puzzles, and guidance reveals different reasoning bottlenecks across models. A human comparison and few-shot analysis further examine model performance.

5 Results

  • 46.4 average: GPT-4V leads single-concept puzzles, while Claude 3 Opus averages 39.4 and Gemini Pro 34.5.GPT-4V scores 67.5 in Numbers; Claude 3 Opus scores 44.5 in Shapes. Models perform similarly on average across single- and dual-concept patterns.Source: §5 · p. 7
  • 45.5 average: GPT-4V leads on dual-concept puzzles, while models perform similarly on average across single- and dual-concept patterns.GPT-4V scores 46.4 on single-concept puzzles; Claude 3 Opus averages 43.7 on dual-concept puzzles.Source: §5 · p. 7

5.1 Analysis of Multimodal Reasoning Bottlenecks

  • With visual-perception and inductive-reasoning explanations, GPT-4V and Claude 3 Opus solve almost all cases; LLaVA-13B and Gemini Pro improve most with all three explanation types.The authors characterize these guided results as an optimistic upper bound; they may not make the puzzles trivial.Source: §5.1 · pp. 7–8, Figure 4 · p. 7

5.2 Comparison to Human Performance

  • 91.6% average human score versus GPT-4V’s 47.5% on the same 40-puzzle sample, completed by 23 university students in 30 minutes.The sample was drawn from 20 puzzle categories.Source: §5.2 · p. 8, Figure 5 · p. 8

5.3 Effect of Few-Shot Demonstrations

  • Few-shot demonstrations generally increase performance, with models usually achieving their best results at the largest number of demonstrations, despite some GPT-4V drops.Demonstrations interleave puzzle images and questions with ground-truth reasoning explanations, sampled from different categories.Source: §5.3 · p. 8, Figure 6 · p. 8

6 Related Work

Related benchmarks address visual reasoning, multimodal understanding, and abstract patterns, but differ in scope and design. PUZZLEVQA emphasizes broad abstract concepts and supports diagnosis of multimodal reasoning.

Related work

  • VQA and OK-VQA evaluate basic perception and reasoning, while MMMU and ScienceQA assess multidisciplinary proficiency involving domain knowledge and multimodal understanding.Source: §6 · p. 9
  • RAVEN uses visual matrices with a specific spatial layout and can be solved exactly with search algorithms, while MiniSCAN is limited to color-based patterns.CLEVR focuses on logic and commonsense in synthetic visual scenarios; ConceptARC focuses on spatial concepts.Source: §6 · p. 9
  • PUZZLEVQA studies perceptual, inductive, and deductive reasoning with broad concepts and includes captions and pattern explanations for diagnosis.Its concepts include numbers, colors, shapes, and size.Source: §6 · p. 9

7 Conclusion

PUZZLEVQA reveals substantial difficulty for large multimodal models on abstract-pattern puzzles requiring perception and inductive and deductive reasoning. Ground-truth explanations identify weaker visual perception and inductive reasoning as GPT-4V’s main bottlenecks, while other models require more guidance.

7 Conclusion

  • GPT-4V’s main reasoning bottlenecks are weaker visual perception and inductive reasoning, while other models require more ground-truth guidance.The experiments also find substantial challenges across puzzles requiring visual perception, inductive reasoning, and deductive reasoning.Source: §7 · p. 9

Limitations

The paper focuses mainly on zero-shot evaluation, with few-shot prompting showing inconsistent benefits. The appendices describe multiple-choice option generation, dataset statistics, and consistent prompt formatting.

Limitations

  • The evaluation mainly uses zero-shot settings; few-shot prompting showed inconsistent benefits and remains an area for future work.The authors note that demonstrations may improve adaptation, but report inconsistent few-shot results.Source: Limitations · p. 10

A.1 Multiple-Choice Format Details

  • Numeric-puzzle options are sampled heuristically from ranges based on the answer’s magnitude; discrete options come from objects represented in the image.Examples use ranges 1–9 for answers below 10 and 1–99 for answers below 100.Source: §A.1 · p. 12

A.2 Dataset Details

A.3 Prompt Examples

  • Figure 7 gives textual prompt examples for eliciting reasoning steps, and the paper uses a consistent prompt format across abstract puzzles.Source: §A.3 · p. 12

A.4 Code Implementation Example

The implementation example constructs a colored hexagon puzzle with a missing triangle, then explains the opposite-color pattern used to infer the answer.

A.4 Code Implementation Example

  • The ColorHexagonPattern implementation defines a color palette and creates a white image canvas at scaled resolution.The image size is 512 pixels with a scale factor of 4.Source: §A.4 · p. 12
  • A hexagon is divided into six colored triangles, with vertices and triangle height computed from the side length and black outlines drawn around the shapes.The side length is one-third of the size, and triangle height uses sqrt(3) / 2 times that length.Source: §A.4 · p. 12
  • The code samples color names, selects a missing answer position, and marks that triangle with a question mark.The example also constructs multiple-choice options for the missing part.Source: §A.4 · p. 12
  • The example explanation observes matching colors in opposite parts, infers that opposite parts share a color, and deduces the missing color.Source: §A.4 · p. 12
  • Figure 7 presents textual prompt examples for eliciting reasoning steps from large multimodal models.Source: Figure 7 · p. 13

A.5 Dataset Size Analysis

The appendix reports that direct prompting is less effective than chain-of-thought prompting for Gemini Pro and GPT-4V. It also examines dataset size and the stability of model comparisons across sample counts.

A.5 Dataset Size Analysis

  • 2000 samples form the dataset: 20 templates with 100 instances each, yielding 200 test samples per puzzle category.The size analysis also evaluates 1000, 2000, and 4000 total samples.Source: §A.5 · p. 13, Table 4 · p. 13
  • Average single-concept scores vary across sample sizes, but the model-performance comparisons do not significantly change.The authors consider the chosen dataset size relatively robust to experimental variance.Source: §A.5 · p. 13, Table 4 · p. 13
  • The puzzles cover numbers, colors, shapes, and size, with both single-concept and dual-concept designs.Figure 3 presents the taxonomy of the puzzle categories.Source: §A.5 · p. 13

A.6 Comparison of CoT and Direct Prompting

  • Direct prompting is less effective for Gemini Pro and GPT-4V than chain-of-thought prompting on single-concept puzzles.Table 5 reports direct-prompting results and changes in average performance compared with chain-of-thought prompting.Source: §A.6 · p. 14, Table 5 · p. 14

B Qualitative Analysis

Two GPT-4V cases expose visual-perception failures and faulty inductive reasoning on abstract-pattern puzzles.

B Qualitative Analysis

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