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New method optimizes LLM-based retrieval models for efficiency

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new method to optimize the sparsity of neural sparse retrieval models, which utilize Large Language Models (LLMs) for semantic term expansion. This approach combines adaptive strategies like learnable soft top-K and per-term thresholding, along with FLOPs regularization, to reduce the length of document and query vectors. Experiments on the MS MARCO and BEIR datasets using the Lion-SP model show significant improvements in retrieval latency and storage costs while maintaining high relevance. AI

IMPACT This research could lead to more efficient and cost-effective information retrieval systems by reducing latency and storage requirements.

RANK_REASON Academic paper detailing a new method for optimizing neural sparse retrieval models. [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 →

New method optimizes LLM-based retrieval models for efficiency

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Academic paper detailing a new method for optimizing neural sparse retrieval models. [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) · Tao Yang ·

    Adaptive Sparsity Optimization with Learnable Soft Top-K and Per-Term Thresholding for Efficient Retrieval

    Recent work on neural sparse retrieval has demonstrated strong relevance by leveraging Large Language Models (LLMs) for semantic term expansion. However, learned models paired with previous sparsification techniques still yield overly long document and query vectors partly due to…

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