PUZZLEVQA: Multimodal Reasoning Challenges of Language Models with Abstract Visual Patterns

Core Concepts

Large multimodal models struggle with abstract reasoning, highlighting weaknesses in visual perception and inductive reasoning.

Abstract

Abstract:

Large multimodal models enhance language models but struggle with general intelligence and reasoning like humans.
PUZZLEVQA evaluates large multimodal models on abstract patterns based on fundamental concepts.
Experiments show that even advanced models like GPT-4V face challenges in solving simple abstract patterns.
Main bottlenecks identified as weaker visual perception and inductive reasoning abilities.
Introduction:

Large language models have shown remarkable capabilities but lack general intelligence and reasoning abilities like humans.
Multimodal models integrate language understanding with visual information for broader capabilities.
Model Input & Output:

Example question involving color concept shows stages of visual perception, inductive reasoning, and deductive reasoning.
Even advanced large multimodal models struggle to understand abstract patterns based on colors, shapes, numbers, and sizes.
PuzzleVQA Dataset:

PUZZLEVQA dataset systematically evaluates reasoning challenges in large multimodal models using abstract pattern puzzles.
Dataset includes diverse puzzles focusing on fundamental concepts like numbers, colors, shapes, and size.
Experimental Setup & Results:

Inference pipeline uses zero-shot chain of thought prompting to elicit reasoning steps from large multimodal models.
Evaluation results show varying performance among different large multimodal models on single-concept and dual-concept abstract patterns.
Analysis & Case Study:

Ground truth explanations improve model performance by providing additional information for reasoning stages.
GPT-4V demonstrates limitations in visual perception and faulty inductive reasoning through case study examples.
Related Work & Conclusion:

PUZZLEVQA focuses on evaluating how large multimodal models mimic cognitive processes for abstract reasoning tasks.
Future research should aim at enhancing the abstract reasoning abilities of large multimodal models.

Stats

大規模なマルチモーダルモデルは、抽象的なパターンに対する理解力に苦戦しており、視覚認識と帰納的推論能力の弱点が浮き彫りになっています。
GPT-4Vは、平均スコア46.4で最も優れた抽象パターンの推論を示しました。
Claude 3 Opusは、「Shapes」カテゴリで最高得点44.5を記録し、全体的な平均は39.4です。
他のモデル（Gemini Pro、LLaVA-13B）はいくつかのカテゴリでランダムベースラインと同様の結果を示しました。

Quotes

Key Insights Distilled From

PuzzleVQA

by Yew Ken Chia... at arxiv.org 03-21-2024

https://arxiv.org/pdf/2403.13315.pdf

Deeper Inquiries

大規模なマルチモーダルモデルが抽象的なパターンに対する理解力に苦戦している理由は何ですか？

大規模なマルチモーダルモデルが抽象的なパターンに苦戦する主な理由は、弱い視覚認識能力と帰紵推論能力の欠如です。研究結果から明らかにされたように、これらのモデルは単純な抽象パターンを適切に認識し、その中から一般原則を適用して特定の問題を解決する際に困難を抱えています。具体的には、ビジュアル知覚段階で画像情報を正しく解釈できず、それが後続の帰紵推論段階で間違った仮説や結論へと導くことが挙げられます。このような認知プロセスの欠陥が大規模マルチモーダルモデルの性能低下や困難さを引き起こす要因と言えます。