ZEBRA Knowledge Base for CommonsenseQA
| Property | Value |
|---|---|
| Base Model | intfloat/e5-base-v2 |
| License | CC BY-NC-SA 4.0 |
| Paper | arXiv:2410.05077 |
| Language | English |
What is zebra-kb-csqa-train?
ZEBRA is an innovative zero-shot retrieval augmentation framework designed specifically for commonsense question answering. This model represents the knowledge base trained on the CommonsenseQA dataset, forming a crucial component of the ZEBRA pipeline which achieves remarkable improvements in accuracy across multiple benchmarks.
Implementation Details
The framework operates through a three-stage pipeline: example retrieval, knowledge generation, and informed reasoning. Built on the E5-base-v2 architecture, it enables efficient retrieval of relevant question-knowledge pairs to enhance the performance of language models in commonsense reasoning tasks.
- Zero-shot capability requiring no task-specific training
- Integrated with popular LLMs like Mistral-7B and Llama-3
- Demonstrated accuracy improvements of 4-6% across multiple datasets
Core Capabilities
- Retrieval of contextually relevant examples for input questions
- Generation of explanatory knowledge for complex queries
- Integration with various language models for enhanced reasoning
- Support for multiple commonsense QA datasets
Frequently Asked Questions
Q: What makes this model unique?
ZEBRA stands out for its ability to improve commonsense reasoning without task-specific training, achieving significant performance gains through its novel retrieval-augmented approach. It demonstrates consistent improvements across various LLM sizes and architectures.
Q: What are the recommended use cases?
The model is particularly suited for commonsense question answering applications, educational tools, and AI systems requiring robust reasoning capabilities. It's especially effective when integrated with language models for tasks requiring deep understanding of everyday knowledge and reasoning.
