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About this paper
Quang Hoang Trung, Le Trung Hoang, Nguyen Van Hoang Phuc
VJ Technology researchers present a two-phase retrieval pipeline for optimizing multi-stage language models for text retrieval, specifically for Japanese legal datasets. This approach combines masked language modeling with contrastive learning and an ensemble method, consistently outperforming traditional sparse retrieval methods like BM25+ and other dense retrieval approaches on domain-specific datasets and demonstrating competitive performance on the MS-MARCO benchmark.
Key insights
- Pre-training with Masked Language Modeling (MLM) on a domain-specific corpus effectively adapts the language model to specialized terminology and contextual relationships.
- A multi-stage fine-tuning process with contrastive learning, particularly with hard negative mining, progressively refines the model's ability to discriminate between relevant and irrelevant documents.
- Combining multiple optimized retrieval models through an ensemble approach systematically boosts overall performance by leveraging diverse strengths.
Summary by alphaXiv.
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