The Pre-Training Study of Expanded-SPLADE Models on Web Document Titles
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 and transfer learning issues for fine-tuning them into Neural Bi-Encoder models. This paper studies the effect of different pre-training datasets and pre-training options on the MLM pre-trained models for retrieval fine-tuning. The study focuses on the SPLADE-style model, which uses the MLM layer also at fine-tuning time. More specifically, we experimented with Expanded-SPLADE (ESPLADE) models, a specific instance of SPLADE models, and in-house web document titl...
| Authors | Hiun Kim, Tae Kwan Lee, Taeryun Won |
|---|---|
| Primary category | cs.IR |
| Published | 2 May 2026 |
| Last updated at source | 2 May 2026 |
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