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Score-only distillation creates compact dense retrieval models

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a score-only distillation method to create compact dense retrieval models. This technique allows a smaller student model to learn ranking behavior from a larger teacher model by only using score vectors, without needing access to the teacher's internal states. The distilled models can achieve up to 50% of the performance gap between the base and teacher models, offering significant speed improvements for query and document encoding. AI

IMPACT Enables more efficient deployment of large retrieval models by creating smaller, faster versions.

RANK_REASON Academic paper detailing a new method for model distillation in information retrieval. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Score-only distillation creates compact dense retrieval models

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Academic paper detailing a new method for model distillation in information retrieval. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Salem Lahlou ·

    Score-Only Distillation for Compact Dense Retrieval

    Large embedding models improve retrieval quality, but serving large encoders online is expensive. We study whether a compact retriever can learn teacher ranking behavior from score vectors without access to teacher hidden states. The student trains on rows built from ground-truth…

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