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Expanded-SPLADE models show limitations in retrieval fine-tuning

By PulseAugur Editorial · [1 sources] ·

This paper investigates the impact of different pre-training datasets and methods on the performance of Expanded-SPLADE (ESPLADE) models for neural information retrieval. The study found that models pre-trained on general corpora with higher learning rates, even with lower Masked Language Modeling accuracies, achieved better retrieval effectiveness. Furthermore, the research indicated that repeating the general pre-training dataset did not significantly improve retrieval effectiveness, and highlighted a trade-off between retrieval cost and effectiveness in highly pruned settings. AI

IMPACT Provides insights into optimizing pre-training strategies for neural information retrieval models, potentially improving search engine performance.

RANK_REASON This is a research paper published on arXiv detailing experimental findings on pre-training methods for information retrieval models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

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

Expanded-SPLADE models show limitations in retrieval fine-tuning

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This is a research paper published on arXiv detailing experimental findings on pre-training methods for information retrieval models. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Hiun Kim, Tae Kwan Lee, Taeryun Won ·

    The Pre-Training Study of Expanded-SPLADE Models on Web Document Titles

    arXiv:2605.01407v1 Announce Type: cross Abstract: Masked Language Modeling (MLM) pre-training is one of the primary ways to initialize Neural Information Retrieval (IR) models prior to retrieval fine-tuning. However, studies show that MLM pre-trained models have limited readiness…

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