ZEBRA Knowledge Base for OBQA Training
| Property | Value |
|---|---|
| Base Model | intfloat/e5-base-v2 |
| License | Creative Commons |
| Paper | arXiv:2410.05077 |
| Language | English |
What is zebra-kb-obqa-train?
ZEBRA (Zero-shot Example-Based Retrieval Augmentation) is an innovative framework designed specifically for commonsense question answering. This particular model represents the knowledge base component trained on the OBQA (OpenBook Question Answering) dataset, serving as a crucial part of ZEBRA's three-stage pipeline architecture.
Implementation Details
The model implements a sophisticated retrieval augmentation framework that operates in three distinct stages: example retrieval, knowledge generation, and informed reasoning. Built upon the E5-base-v2 architecture, it specializes in retrieving relevant question-knowledge pairs to enhance commonsense reasoning capabilities.
- Leverages example-based retrieval for contextual understanding
- Integrates with large language models for knowledge generation
- Supports zero-shot learning capabilities
- Implements state-of-the-art retrieval mechanisms
Core Capabilities
- Question-knowledge pair retrieval
- Contextual example matching
- Integration with multiple LLM backends
- Support for multiple-choice question answering
- Performance improvements of 5-10% over baseline models
Frequently Asked Questions
Q: What makes this model unique?
ZEBRA's unique approach lies in its ability to combine retrieval-based learning with zero-shot capabilities, showing significant improvements in commonsense reasoning without requiring task-specific training. On the OBQA dataset specifically, it has demonstrated improvements of up to 7% in accuracy compared to base models.
Q: What are the recommended use cases?
This model is particularly well-suited for commonsense question answering tasks, especially in scenarios requiring deep understanding of everyday knowledge and reasoning. It's ideal for educational applications, AI assistants, and research in natural language understanding.
