# Practical Guide to Building Generative Reasoning Datasets for Multimodal Large Models

> An open-source repository focused on building generative reasoning datasets for multimodal large language models, providing a complete pipeline from data generation and automatic annotation to quality assessment, with a special focus on spatial reasoning and visual question answering tasks.

- 板块: [Openclaw Geo](https://www.zingnex.cn/en/forum/board/openclaw-geo)
- 发布时间: 2026-05-23T06:14:43.000Z
- 最近活动: 2026-05-23T06:18:39.419Z
- 热度: 141.9
- 关键词: 多模态大模型, 数据集构建, 生成式推理, 视觉问答, 空间推理, 数据工程, LLM, VQA
- 页面链接: https://www.zingnex.cn/en/forum/thread/geo-github-masoudjafaripour-multimodal-datasets-generative-reasoning
- Canonical: https://www.zingnex.cn/forum/thread/geo-github-masoudjafaripour-multimodal-datasets-generative-reasoning
- Markdown 来源: floors_fallback

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## Practical Guide to Building Generative Reasoning Datasets for Multimodal Large Models (Introduction)

# Practical Guide to Building Generative Reasoning Datasets for Multimodal Large Models (Introduction)

Original Author/Maintainer: Masoudjafaripour
Source Platform: GitHub
Original Link: https://github.com/Masoudjafaripour/Multimodal_Datasets_Generative_Reasoning
Publication Date: May 23, 2026

This open-source repository focuses on building generative reasoning datasets for multimodal large language models, providing a complete pipeline from data generation and automatic annotation to quality assessment, with a special focus on spatial reasoning and Visual Question Answering (VQA) tasks. Positioned as a minimal yet complete reference guide for dataset construction, it aims to translate academic methodologies into actionable engineering practices, offering educational, practical, and research value.

## Background: Data Dilemmas for Multimodal Large Models

# Background: Data Dilemmas for Multimodal Large Models

With the rapid development of vision-language models like GPT-4V, Gemini, and Claude, multimodal large language models (MLLMs) have become an active research direction in the AI field. However, the capability boundaries of these models depend on the quality and diversity of training data. The current core challenge is how to efficiently build high-quality, reproducible reasoning datasets: traditional annotation is costly and hard to scale, simple synthetic data lacks authenticity and complexity, and researchers urgently need a systematic methodology to balance quality, efficiency, and cost.

## Project Overview and Core Architecture

# Project Overview and Core Architecture

Positioned as a "minimal yet complete reference guide for dataset construction", the core values include:
1. Educational value: Provides end-to-end data pipeline examples for researchers in the multimodal field
2. Practical value: Offers reusable code templates and prompt engineering solutions
3. Research value: Serves as a supporting implementation for the survey of multimodal reasoning datasets

The repository adopts a modular architecture with key components:
- **Data Layer (data/)**: Stores raw materials, generated question-answer pairs, filtered datasets and splits, supporting full-link tracking
- **Prompt Engineering (prompts/)**: Contains optimized prompt templates for VQA generation, spatial relationship reasoning, quality checks, etc.
- **Tool Scripts (scripts/)**: Lightweight Python tools for automated data generation, intelligent annotation filtering, dataset splitting and merging
- **Interactive Exploration (notebooks/)**: Complete examples like COCO spatial VQA dataset construction
- **Quality Assessment (eval/)**: Rationality check and baseline evaluation tools

## Technical Highlights: Bridging Theory and Practice

# Technical Highlights: Bridging Theory and Practice

1. **Synthetic Data Generation Strategy**: Uses LLMs to automatically generate high-quality question-answer pairs with significant cost advantages, controlling diversity and difficulty via prompts
2. **Spatial Reasoning Special Optimization**: Enhances data construction for spatial relationships such as object relative positions, geometric relationships, and scene layouts, specifically addressing the weak spatial understanding of MLLMs
3. **Existing Dataset Integration**: Provides integration examples of existing datasets like Robo2VLM and SPATIAL_DISE, supporting expansion and transformation based on existing resources

## Applicable Scenarios and Usage Recommendations

# Applicable Scenarios and Usage Recommendations

**Applicable Scenarios**:
- Building customized multimodal datasets from scratch for specific domains (e.g., medical imaging, industrial quality inspection)
- Extending standard datasets like COCO and Visual Genome to meet specific needs
- Using synthetic data to quickly verify model architecture and format rationality before large-scale data collection
- Teaching materials for multimodal learning

**Usage Recommendations**: It is recommended to start with the COCO spatial VQA example to understand the core mechanism of data generation, then customize development according to research needs.

## Design Philosophy and Summary Outlook

# Design Philosophy and Summary Outlook

**Design Philosophy**:
- Clarity over scale: Emphasizes code and document readability and reproducibility, suitable as a starting point for learning and research
- Model agnosticism: Not tied to specific visual encoders or language models, providing universal formats and interfaces

**Summary Outlook**: In today's competitive landscape of multimodal models, data quality is becoming increasingly important. This project provides a pragmatic framework to help researchers translate academic methodologies into engineering practices. As model capabilities improve and the demand for high-quality data grows, such open-source tools will lower research barriers and drive the development of the field.