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STORM framework enhances lexical query expansion for retrieval

By PulseAugur Editorial · [2 sources] ·

Researchers have developed STORM, a self-supervised framework for lexical query expansion that improves information retrieval. This method uses a reward-guided beam search to optimize token generation, making it more effective for retrieval tasks. STORM offers a competitive, infrastructure-light alternative to dense neural retrieval systems, achieving strong performance across various benchmarks and languages. AI

IMPACT Offers a more efficient and infrastructure-light alternative to dense neural retrieval, potentially improving search performance across many languages.

RANK_REASON The cluster contains an academic paper detailing a new method for information retrieval.

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Arthur Satouf, Giulio D'Erasmo, Yuxuan Zong, Habiboulaye Amadou Boubacar, Pablo Piantanida, Benjamin Piwowarski ·

    STORM: Stepwise Token Optimization with Reward-Guided Beam Search

    arXiv:2606.10621v1 Announce Type: cross Abstract: Modern retrieval increasingly relies on dense and learned-sparse neural models that are effective but require encoding the entire corpus into a specialized index, rebuilt whenever the model changes. Lexical retrievers like BM25 st…

  2. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Benjamin Piwowarski ·

    STORM: Stepwise Token Optimization with Reward-Guided Beam Search

    Modern retrieval increasingly relies on dense and learned-sparse neural models that are effective but require encoding the entire corpus into a specialized index, rebuilt whenever the model changes. Lexical retrievers like BM25 stay efficient and transparent on a standard inverte…

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