WaltonFuture/geometry3k-in-context-synthesizing

Dataset

This dataset is used for unsupervised post-training of multi-modal large language models (MLLMs). It contains image-text pairs where the 'problem' field presents a question requiring reasoning and the 'answer' field provides a solution. This data supports the MM-UPT framework detailed in the associated paper.

2

5 commits

1 linked in READMEs

updated Jun 2, 2025

See the code

README

This dataset is used for unsupervised post-training of multi-modal large language models (MLLMs). It contains image-text pairs where the 'problem' field presents a question requiring reasoning and the 'answer' field provides a solution. This data supports the MM-UPT framework detailed in the associated paper.

The dataset contains 2101 examples in the training split. Each example includes:

  • images: Image data.
  • problem: A multi-modal problem requiring reasoning.
  • answer: The solution to the problem.

Contributors

WaltonFuture

4 commits

nielsr

1 commits

WaltonFuture/geometry3k-in-context-synthesizing

Dataset

This dataset is used for unsupervised post-training of multi-modal large language models (MLLMs). It contains image-text pairs where the 'problem' field presents a question requiring reasoning and the 'answer' field provides a solution. This data supports the MM-UPT framework detailed in the associated paper.

2

5 commits

1 linked in READMEs

updated Jun 2, 2025

See the code

README

This dataset is used for unsupervised post-training of multi-modal large language models (MLLMs). It contains image-text pairs where the 'problem' field presents a question requiring reasoning and the 'answer' field provides a solution. This data supports the MM-UPT framework detailed in the associated paper.

The dataset contains 2101 examples in the training split. Each example includes:

  • images: Image data.
  • problem: A multi-modal problem requiring reasoning.
  • answer: The solution to the problem.

Contributors

WaltonFuture

4 commits

nielsr

1 commits