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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cs.IR 2years
2026 2representative citing papers
Structured negative mining with taxonomy and LLM judges improves offline category accuracy by 2.6% in IKEA search but yields no significant online engagement gains due to prevalent zero-click user behavior.
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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.
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Negative Data Mining for Contrastive Learning in Dense Retrieval at IKEA.com
Structured negative mining with taxonomy and LLM judges improves offline category accuracy by 2.6% in IKEA search but yields no significant online engagement gains due to prevalent zero-click user behavior.