Enterprise AI Analysis
Alibaba International E-commerce Product Search Competition DcuRAGONS Team Technical Report
Explore how our DcuRAGONS team leveraged advanced multilingual LLMs to achieve the highest score in the Alibaba International E-commerce Product Search Competition.
Dominating Multilingual E-commerce Search with Advanced LLMs
Our DcuRAGONS team achieved the highest score in the Alibaba International E-commerce Product Search Competition, leveraging a data-centric methodology combining multilingual LLMs, task-adaptive pre-training, and robust cross-validation strategies to overcome complex challenges.
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Competition Win Rate
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Peak QC F1 Score
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Peak QI F1 Score
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TAPT QC F1 Improvement
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Languages Supported
Deep Analysis & Enterprise Applications
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Overall Approach
Model Performance
Training Strategy
Data Handling
Challenges & Solutions
Core Methodology Flow
Systematic Multilingual LLM Comparison
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Translation Augmentation (TA)
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Task-Adaptive Pre-training (TAPT)
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Category-Aware Query-Grouped Cross-Validation (CA Split)
Our strategy involved a systematic comparison of multilingual LLMs, leveraging translation augmentation, a two-stage task-adaptive pre-training, and a novel cross-validation strategy.
Leading Performance Highlights
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Peak QC F1 Score Achieved
Our approach achieved a top QC F1 score of 89.36% and QI F1 of 88.81% on the private leaderboard, demonstrating robust performance in multilingual e-commerce search.
Multilingual LLM Selection & Scaling
Larger multilingual LLMs like Gemma-3-12B consistently outperformed smaller architectures. Scaling from XLM-Roberta to Gemma-2-9B improved QC performance by over six points (82.0% → 88.8%), underscoring the importance of robust multilingual foundations and model scale, though with diminishing returns at the highest end.
Two-Stage Training Impact
Task-Adaptive Pre-training (TAPT) proved critical, boosting QC F1 by +0.14% and QI F1 by +0.02% on the private leaderboard. This two-stage process effectively specializes the model to the e-commerce domain, enhancing generalization and stability.
Robust Cross-Validation & Augmentation
The category-aware, query-grouped cross-validation split strategy (CA split) was crucial for preventing data leakage and ensuring accurate generalization. Additionally, translation augmentation (TA) boosted cross-lingual alignment, especially for low-resource languages.
Addressing E-commerce Search Complexities
The competition tackled challenges such as noisy/code-mixed queries, low-resource languages, and hierarchical product categories. Our solutions included leveraging powerful multilingual LLMs, translation augmentation, task-adaptive pre-training, and a specialized cross-validation strategy.
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