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Visual CoT: Advancing Multi-Modal Language Models with a Comprehensive Dataset and Benchmark for Chain-of-Thought Reasoning logo

Visual CoT: Advancing Multi-Modal Language Models with a Comprehensive Dataset and Benchmark for Chain-of-Thought Reasoning

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Advancing multi-modal LLMs with interpretable chain-of-thought reasoning

FreeFree tier
Inputs: image
Type
Open Source
Visit Website GitHub (40 stars)

About Visual CoT: Advancing Multi-Modal Language Models with a Comprehensive Dataset and Benchmark for Chain-of-Thought Reasoning

Visual CoT is a large-scale dataset and benchmark designed to advance chain-of-thought reasoning in multi-modal large language models (MLLMs). The dataset comprises 438k question-answer pairs, each annotated with intermediate bounding boxes that highlight key visual regions relevant to answering the question; approximately 98k of these pairs also include detailed reasoning steps. The authors propose a multi-turn processing pipeline that dynamically focuses on visual inputs and generates interpretable chain-of-thought thoughts. A corresponding benchmark evaluates MLLMs on tasks requiring specific local region identification. The dataset, benchmark, and pre-trained models are publicly available to support further research in improving MLLM interpretability and handling complex visual inputs.

Key Features

Large-scale dataset with 438k question-answer pairs
Annotations with intermediate bounding boxes highlighting key regions
98k pairs with detailed reasoning steps
Multi-turn processing pipeline for dynamic visual focus
Benchmark for evaluating local region identification in MLLMs
Publicly available dataset, benchmark, and pre-trained models

Pros & Cons

Pros
  • Provides interpretable reasoning through intermediate bounding boxes and steps
  • Large-scale dataset with diverse annotations
  • Specifically addresses model limitations on complex visual inputs
  • Includes a dedicated benchmark for standardized evaluation
  • Open-source and publicly available for free
Cons
  • Primarily focuses on local region identification, not general visual reasoning
  • Multi-turn processing pipeline may increase computational overhead

Best For

Improving interpretability of multi-modal language modelsEnhancing performance on visual question answering with high-resolution or small regionsTraining and evaluating chain-of-thought reasoning in vision-language tasksResearch on dynamic visual attention and reasoning step generation

FAQ

What is Visual CoT?
Visual CoT is a dataset and benchmark for chain-of-thought reasoning in multi-modal large language models. It includes 438k question-answer pairs with bounding box annotations and reasoning steps to improve interpretability and performance on complex visual inputs.
What does the Visual CoT dataset contain?
The dataset contains 438k question-answer pairs, each with intermediate bounding boxes highlighting key image regions, and approximately 98k pairs also include detailed reasoning steps.
Is Visual CoT publicly available?
Yes, the Visual CoT dataset, benchmark, and pre-trained models are publicly available via the project page linked in the paper.
What problem does Visual CoT address?
It addresses the lack of interpretability in multi-modal LLMs and their difficulty handling high-resolution images or small regions of interest that are crucial for answering questions.