--- title: "Who cites SPLADE v2: Sparse Lexical and — 72 citing papers" description: "72 papers of the Elamur Research library cite «SPLADE v2: Sparse Lexical and Expansion Model for Information Retrieval», found in their…" canonical: "https://research.elamur.ai/free/en/papers/formal-2021-splade-v2-sparse-lexical/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/formal-2021-splade-v2-sparse-lexical/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [SPLADE v2: Sparse Lexical and Expansion Model for…](https://md.research.elamur.ai/free/en/papers/formal-2021-splade-v2-sparse-lexical/) / cited by # Who cites «SPLADE v2: Sparse Lexical and Expansion Model for Information Retrieval» 72 papers of our library cite it (of the 149,439 whose bibliographies we read) · 12 with a page here, the most cited first · [← back to «SPLADE v2: Sparse Lexical and Expansion Model for Information Retrieval»](https://md.research.elamur.ai/free/en/papers/formal-2021-splade-v2-sparse-lexical/) 1. [**Unsupervised Dense Information Retrieval with Contrastive Learning arXiv:2112.09118**Gautier Izacard, Mathilde Caron, Lucas Hosseini, et al.*2021 · 836 citations*](https://md.research.elamur.ai/free/en/papers/izacard-2021-unsupervised-dense-information-retrieval-contrastive/) 2. **[ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction](https://md.research.elamur.ai/free/en/papers/santhanam-2022-colbertv2-effective-efficient-retrieval-lightweight/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Keshav Santhanam, Omar Khattab, Jon Saad-Falcon, et al.*2022 · 485 citations* 3. [**Text and Code Embeddings by Contrastive Pre-Training arXiv:2201.10005**Arvind Neelakantan, Tao Xu, Raul Puri, et al.*2022 · 323 citations*](https://md.research.elamur.ai/free/en/papers/neelakantan-2022-text-code-embeddings-contrastive-pre/) 4. [**SGPT: GPT Sentence Embeddings for Semantic Search arXiv:2202.08904**Niklas Muennighoff*2022 · 189 citations*](https://md.research.elamur.ai/free/en/papers/muennighoff-2022-sgpt-gpt-sentence-embeddings-semantic/) 5. **[On the Risk of Misinformation Pollution with Large Language Models](https://md.research.elamur.ai/free/en/papers/pan-2023-risk-misinformation-pollution-large-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yikang Pan, Liangming Pan, Wenhu Chen, et al.*2023 · 179 citations* 6. [**Promptagator: Few-shot Dense Retrieval From 8 Examples arXiv:2209.11755**Zhuyun Dai, Vincent Y. Zhao, Ji Ma, et al.*2022 · 170 citations*](https://md.research.elamur.ai/free/en/papers/dai-2022-promptagator-few-shot-dense-retrieval/) 7. **[How to Train Your Dragon: Diverse Augmentation Towards Generalizable Dense Retrieval](https://md.research.elamur.ai/free/en/papers/lin-2023-how-train-your-dragon-diverse/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Sheng-Chieh Lin, Akari Asai, Minghan Li, et al.*2023 · 116 citations* 8. **[AI4Research: A Survey of Artificial Intelligence for Scientific Research](https://md.research.elamur.ai/free/en/papers/chen-2025-ai4research-survey-artificial-intelligence/) [Science China Information Sciences](https://md.research.elamur.ai/free/en/venues/science-china-information-sciences/)**Qiguang Chen, Mingda Yang, Libo Qin, et al.*2025 · 21 citations* 9. **[Optimized Text Embedding Models and Benchmarks for Amharic Passage Retrieval](https://md.research.elamur.ai/free/en/papers/mekonnen-2025-optimized-text-embedding-models-benchmarks/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Kidist Amde Mekonnen, Yosef Worku Alemneh, Maarten de Rijke*2025 · 4 citations* 10. **[QueStER: Query Specification for Generative Keyword-Based Retrieval](https://md.research.elamur.ai/free/en/papers/satouf-2026-quester-query-specification-generative-keyword/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Arthur Satouf, Yuxuan Zong, Habiboulaye Amadou Boubacar, et al.*2026 · 1 citation* 11. [**AmharicIR+Instr: A Two-Dataset Resource for Neural Retrieval and Instruction Tuning arXiv:2602.09914**Tilahun Yeshambel, Moncef Garouani, Josiane Mothe*2026*](https://md.research.elamur.ai/free/en/papers/yeshambel-2026-amharicir-instr-two-dataset-resource/) 12. **[SYMDIREC: A Neuro-Symbolic Divide-Retrieve-Conquer Framework for Enhanced RTL Synthesis and Summarization](https://md.research.elamur.ai/free/en/papers/vijayaraghavan-2026-symdirec-neuro-symbolic-divide-retrieve/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Prashanth Vijayaraghavan, Apoorva Nitsure, Luyao Shi, et al.*2026* 60 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/formal-2021-splade-v2-sparse-lexical/) — SPLADE v2: Sparse Lexical and Expansion Model for Information Retrieval