Alibaba International E-commerce Product Search Competition DILAB Team Technical Report
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Abstract (arXiv)
This study presents the multilingual e-commerce search system developed by the DILAB team, which achieved 5th place on the final leaderboard with a competitive overall score of 0.8819, demonstrating stable and high-performing results across evaluation metrics. To address challenges in multilingual query-item understanding, we designed a multi-stage pipeline integrating data refinement, lightweight preprocessing, and adaptive modeling. The data refinement stage enhanced dataset consistency and category coverage, while language tagging and noise filtering improved input quality. In the modeling phase, multiple architectures and fine-tuning strategies were explored, and hyperparameters optimized using curated validation sets to balance performance across query-category (QC) and query-item (QI) tasks. The proposed framework exhibited robustness and adaptability across languages and domains, highlighting the effectiveness of systematic data curation and iterative evaluation for multilingual search systems. The source code is available at https://github.com/2noweyh/DILAB-Alibaba-Ecommerce-Search.
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References · 7
- 2024The Llama 3 Herd of Modelscited by 14,192
- 2024Superfiltering: Weak-to-Strong Data Filtering for Fast Instruction-Tuningcited by 134
- 2025Automated Query-Product Relevance Labeling using Large Language Models for E-commerce Searchcited by 5
- 2022A Semantic Alignment System for Multilingual Query-Product Retrievalcited by 3
- 2022Modified Query Expansion Through Generative Adversarial Networks for Information Extraction in E-Commercecited by 1
- 2024GPT-4o as the Gold Standard: A Scalable and General Purpose Approach to Filter Language Model Pretraining Datacited by 1
- 2025CSRM-LLM: Embracing Multilingual LLMs for Cold-Start Relevance Matching in Emerging E-commerce Marketscited by 1
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