Naver Labs Europe (SPLADE) @ TREC Deep Learning 2022
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Abstract (arXiv)
This paper describes our participation to the 2022 TREC Deep Learning challenge. We submitted runs to all four tasks, with a focus on the full retrieval passage task. The strategy is almost the same as 2021, with first stage retrieval being based around SPLADE, with some added ensembling with ColBERTv2 and DocT5. We also use the same strategy of last year for the second stage, with an ensemble of re-rankers trained using hard negatives selected by SPLADE. Initial result analysis show that the strategy is still strong, but is still unclear to us what next steps should we take.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Feb 2023.
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Citing papers per year: 2024: 2; 2025: 2.
4 papers.
- 2025Provence: efficient and robust context pruning for retrieval-augmented generationcited by 25
- 2024DiSCo: LLM Knowledge Distillation for Efficient Sparse Retrieval in Conversational Searchcited by 8
- 2024IRLab@iKAT24: Learned Sparse Retrieval with Multi-aspect LLM Query Generation for Conversational Searchcited by 3
- 2025Adaptive Personalized Conversational Information Retrievalcited by 0
- 2025Adaptive Personalized Conversational Information Retrieval12 Aug 2025
- 2025Provence: efficient and robust context pruning for retrieval-augmented generation27 Jan 2025
- 2024IRLab@iKAT24: Learned Sparse Retrieval with Multi-aspect LLM Query Generation for Conversational Search22 Nov 2024
- 2024DiSCo: LLM Knowledge Distillation for Efficient Sparse Retrieval in Conversational Search18 Oct 2024