Iterative Definition Refinement for Zero-Shot Classification via LLM-Based Semantic Prototype Optimization
Web filtering systems rely on accurate web content classification to block cyber threats, prevent data exfiltration, and ensure compliance. However, classification is increasingly difficult due to the dynamic and rapidly evolving nature of the modern web. Embedding-based zero-shot approaches map content and category descriptions into a shared semantic space, enabling label assignment without labeled training data, but remain highly sensitive to definition quality. Poorly specified or ambiguous definitions create semantic overlap in the embedding space, leading to systematic misclassification. In this paper, we propose a training-free, adaptive iterative definition refinement framework tha...
| Authors | Naeem Rehmat, Muhammad Saad Saeed, Ijaz Ul Haq et al. |
|---|---|
| Primary category | cs.CV |
| Published | 30 April 2026 |
| Last updated at source | 30 April 2026 |
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