ZEBRA: Zero-Shot Example-Based Retrieval Augmentation
| Property | Value |
|---|---|
| Base Model | intfloat/e5-base-v2 |
| License | CC BY-NC-SA 4.0 |
| Language | English |
| Paper | arXiv:2410.05077 |
What is zebra-kb-csqa2-train?
ZEBRA is an advanced framework designed for zero-shot commonsense question answering, utilizing a three-stage pipeline approach: example retrieval, knowledge generation, and informed reasoning. This specific model represents the training knowledge base for the CSQA2 dataset, forming part of the larger ZEBRA ecosystem.
Implementation Details
The framework operates through a sophisticated pipeline that leverages retrieved examples to generate contextual knowledge and perform reasoning. It's built on the e5-base-v2 architecture and can be integrated with various language models like Meta-Llama-3-8B-Instruct.
- Example retrieval system for finding relevant question-knowledge pairs
- Knowledge generation component for explicating commonsense reasoning
- Informed reasoning mechanism for answer selection
Core Capabilities
- Zero-shot commonsense question answering
- Retrieval-augmented reasoning
- Multiple-choice answer selection
- Explanatory knowledge generation
- Performance improvements across various benchmarks
Frequently Asked Questions
Q: What makes this model unique?
ZEBRA stands out for its ability to improve commonsense reasoning without fine-tuning, showing significant performance gains across multiple benchmarks. For instance, it improved Mistral-7B-Instruct's average accuracy from 68.9% to 73.5% across various datasets.
Q: What are the recommended use cases?
The model is specifically designed for commonsense question answering tasks, particularly those requiring contextual understanding and reasoning. It's ideal for applications needing explainable AI decisions in educational, cognitive assessment, or automated reasoning systems.
