LUMA: A Benchmark Dataset for Multimodal and Uncertainty Quantification

LUMA is a multimodal dataset including audio, text, and image modalities, intended for benchmarking multimodal learning and multimodal uncertainty quantification. The dataset was authored by bezirganyan and last updated on 2025 -08-14. The full description and code are available on the dataset's Hugging Face page.

Use Cases

• Benchmarking multimodal fusion models based on the described audio, text, and image data.
• Developing uncertainty quantification methods for multimodal predictions.
• Training models to handle ambiguous or uncertain information across different data types.
• Comparing model performance on a dedicated multimodal benchmark.

Strengths

• Explicitly designed for benchmarking multimodal learning and uncertainty quantification.
• Includes multiple data modalities: audio, text, and images.
• Last updated on 2025-08-14, indicating recent maintenance.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count, file formats, and license are unknown, which may limit suitability assessment.

Provenance

Source

bezirganyan on Hugging Face

Collection Method

Method of data gathering is not described.

Time Range

null

Freshness

Last updated 2025-08-14 16:23:43; freshness should be verified.

Geography

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