ZEBRA Knowledge Base for QASC Training
| Property | Value |
|---|---|
| Base Model | intfloat/e5-base-v2 |
| License | Creative Commons |
| Language | English |
| Paper | arXiv:2410.05077 |
What is zebra-kb-qasc-train?
ZEBRA (Zero-shot Example-Based Retrieval Augmentation) is an innovative framework designed for commonsense question answering. This specific model represents the knowledge base component trained on the QASC dataset, serving as a crucial part of ZEBRA's three-stage pipeline: example retrieval, knowledge generation, and informed reasoning.
Implementation Details
Built on the E5-base-v2 architecture, this model functions as a specialized retrieval system that helps identify relevant question-knowledge pairs from a large collection. It's part of a larger ecosystem that demonstrates significant improvements in accuracy across multiple commonsense QA benchmarks.
- Integrates seamlessly with popular language models like Mistral-7B, Phi3, and Llama-3
- Implements a sophisticated retrieval augmentation pipeline
- Supports zero-shot learning capabilities
Core Capabilities
- Efficient retrieval of relevant example pairs for knowledge generation
- Supports complex question answering tasks
- Enables improved accuracy in commonsense reasoning
- Facilitates context-aware answer generation
Frequently Asked Questions
Q: What makes this model unique?
This model is part of ZEBRA's innovative approach that combines example retrieval with knowledge generation, showing significant improvements in accuracy (up to +4.6% average improvement across datasets) without requiring fine-tuning of the base language model.
Q: What are the recommended use cases?
The model is specifically designed for commonsense question answering tasks, particularly useful in educational applications, automated reasoning systems, and AI-powered knowledge assessment tools.
