RLAIF-V-Dataset is a large-scale multimodal feedback dataset created by unsloth. It provides 83,132 preference pairs, where instructions are collected from a diverse set of sources. The dataset was last updated on Hugging Face on 2024-09 26.
Use Cases
- Training reward models for multimodal reinforcement learning from AI feedback (RLAIF) based on the preference pairs.
- Fine-tuning vision-language models for improved alignment based on high-quality human or AI feedback.
- Benchmarking the performance of multimodal large language models (MLLMs) on preference-based tasks.
Strengths
- Contains 83,132 preference pairs, indicating a substantial scale.
- Described as providing high-quality feedback.
- Instructions are collected from a diverse set of sources, suggesting variety.
Limitations
- Column-level documentation is absent; field semantics must be inferred after download.
- Row count is unknown, which may limit suitability assessment.
- Description metadata is limited; actual data quality requires manual inspection after download.
Provenance
- Source
- unsloth
- Collection Method
- Instructions collected from a diverse set of sources; feedback likely generated via AI or human annotation.
- Time Range
- null
- Freshness
- Last updated 2024-09-26 01:39:43; freshness should be verified.
- Geography
- null