  Zero-shot text learning enables text classifiers to handle unseen classes
efficiently, alleviating the need for task-specific training data. A simple
approach often relies on comparing embeddings of query (text) to those of
potential classes. However, the embeddings of a simple query sometimes lack
rich contextual information, which hinders the classification performance.
Traditionally, this has been addressed by improving the embedding model with
expensive training. We introduce QZero, a novel training-free knowledge
augmentation approach that reformulates queries by retrieving supporting
categories from Wikipedia to improve zero-shot text classification performance.
Our experiments across six diverse datasets demonstrate that QZero enhances
performance for state-of-the-art static and contextual embedding models without
the need for retraining. Notably, in News and medical topic classification
tasks, QZero improves the performance of even the largest OpenAI embedding
model by at least 5% and 3%, respectively. Acting as a knowledge amplifier,
QZero enables small word embedding models to achieve performance levels
comparable to those of larger contextual models, offering the potential for
significant computational savings. Additionally, QZero offers meaningful
insights that illuminate query context and verify topic relevance, aiding in
understanding model predictions. Overall, QZero improves embedding-based
zero-shot classifiers while maintaining their simplicity. This makes it
particularly valuable for resource-constrained environments and domains with
constantly evolving information.
