Llava Sae Explanations 5K: Feature Interpretations for a Vision-Language Model

5,000 feature explanations generated for a 131k sparse autoencoder trained on the llava-next-llama3-8B vision-language model. The dataset includes two versions: a 'revised' set using an updated prompt and cached data, and a 'legacy' set using an older prompt and a subset of the LLaVA-NeXT-Data. It was created by lmms-lab and last updated on November 22, 2024.

Use Cases

• Evaluating the quality of feature explanations based on the revised prompt methodology described.
• Comparing feature interpretations between the revised and legacy explanation sets.
• Probing the semantic meaning of features extracted from a vision-language model's activations.
• Training or benchmarking automated feature explanation systems for sparse autoencoders.

Strengths

• Contains 5,000 explanations for a specific 131k sparse autoencoder.
• Provides two distinct explanation sets (revised and legacy) for comparative analysis.
• The revised explanations were used for reported evaluation results in a paper.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The dataset's scope is limited to the first 5k features of a specific autoencoder.

Provenance

Source

lmms-lab

Collection Method

Generated via explanation prompts applied to features from a sparse autoencoder trained on llava-next-llama3-8B.

Freshness

Last updated 2024-11-22 12:51:18; freshness should be verified.