ZEBRA Knowledge Base - ARC Train
| Property | Value |
|---|---|
| Base Model | intfloat/e5-base-v2 |
| License | Creative Commons |
| Paper | arXiv:2410.05077 |
| Language | English |
What is zebra-kb-arc-train?
ZEBRA-KB-ARC-Train is a specialized knowledge base component of the ZEBRA framework, designed for zero-shot example-based retrieval augmentation in commonsense question answering. Built upon the E5-base-v2 architecture, it serves as a crucial resource for training on the ARC dataset, enabling enhanced performance in question answering tasks.
Implementation Details
The model implements a three-stage pipeline approach: example retrieval, knowledge generation, and informed reasoning. It leverages sophisticated retrieval mechanisms to find relevant question-knowledge pairs from a comprehensive collection.
- Built on E5-base-v2 architecture for robust retrieval capabilities
- Integrates seamlessly with the ZEBRA pipeline framework
- Optimized for ARC dataset training scenarios
- Supports zero-shot learning applications
Core Capabilities
- Example-based retrieval for commonsense reasoning
- Knowledge pair matching and extraction
- Support for multi-step reasoning processes
- Integration with various LLM backends
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its specialized focus on the ARC dataset and its ability to enhance LLM performance through example-based retrieval. It's particularly effective at improving accuracy in commonsense question answering tasks without requiring task-specific fine-tuning.
Q: What are the recommended use cases?
The model is ideal for educational applications, research in commonsense reasoning, and development of QA systems. It's particularly effective when integrated with larger language models like Mistral-7B or Llama-3 for enhanced performance in commonsense question answering.
