Proves Voronoi complexity equals sign-rank for top-1 retrieval, introduces CUS diagnostic predicting retrieval failure at AUC >0.8 without labels, and AT-DW-InfoNCE objective with derived alpha^*=2.0 that improves Recall@100 on synthetic data.
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cs.IR 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
The paper introduces a seed-guided contrastive framework that uses LLMs to generate realistic synthetic queries and topicality labels for cold-start natural language search, outperforming no-seed and InPars baselines on realism metrics and producing harder evaluation sets.
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The Voronoi Bottleneck: Capacity-Aware Dense Retrieval for Product Search
Proves Voronoi complexity equals sign-rank for top-1 retrieval, introduces CUS diagnostic predicting retrieval failure at AUC >0.8 without labels, and AT-DW-InfoNCE objective with derived alpha^*=2.0 that improves Recall@100 on synthetic data.
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Bridging the Cold-Start Gap: LLM-Powered Synthetic Data Generation for Natural Language Search at Airbnb
The paper introduces a seed-guided contrastive framework that uses LLMs to generate realistic synthetic queries and topicality labels for cold-start natural language search, outperforming no-seed and InPars baselines on realism metrics and producing harder evaluation sets.