Pisc Tr: Multimodal Question Answering with Chain-of-Thought Data

A multimodal dataset from the LLaVA-CoT project, likely containing image-question-answer pairs structured for visual reasoning tasks. The dataset includes a train.jsonl file with conversation data linking images to questions and answers, suggesting a format for training or evaluating vision-language models. It was authored by 'berhaan' and last updated on 2026-01-17.

Use Cases

• Training vision-language models for visual question answering based on the described image-question-answer structure.
• Evaluating model performance on chain-of-thought reasoning tasks based on the dataset's implied purpose.
• Benchmarking multimodal AI systems on tasks requiring object counting or description in images, as suggested by the example question.

Strengths

• Dataset is associated with a published research paper (LLaVA-CoT on arXiv) and a GitHub repository, indicating a research foundation.
• The structure includes both image paths and conversational text, supporting multimodal analysis.

Limitations

• Description metadata is limited; actual data quality requires manual inspection after download.
• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.

Provenance

Source

LLaVA-CoT GitHub Repository

Freshness

Last updated 2026-01-17 02:28:20; freshness should be verified.