RRK compresses documents to multi-token embeddings for efficient listwise reranking, enabling an 8B model to achieve 3x-18x speedups over smaller models with comparable or better effectiveness.
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AutoRelAnnotator routes queries through fine-tuned classifier cascades with isotonic calibration to deliver high-accuracy relevance labels at roughly half the compute cost while adding a small accuracy gain.
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Efficient Listwise Reranking with Compressed Document Representations
RRK compresses documents to multi-token embeddings for efficient listwise reranking, enabling an 8B model to achieve 3x-18x speedups over smaller models with comparable or better effectiveness.
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AutoRelAnnotator: Calibrated Model Cascades for Cost-Efficient Relevance Evaluation in Sponsored Search
AutoRelAnnotator routes queries through fine-tuned classifier cascades with isotonic calibration to deliver high-accuracy relevance labels at roughly half the compute cost while adding a small accuracy gain.