---
title: "Who cites FollowIR: Evaluating and — 30 citing papers"
description: "30 papers of the Elamur Research library cite «FollowIR: Evaluating and Teaching Information Retrieval Models to Follow Instructions», found…"
canonical: "https://research.elamur.ai/free/en/papers/weller-2025-followir-evaluating-teaching-information-retrieval/cited-by/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/weller-2025-followir-evaluating-teaching-information-retrieval/cited-by/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [FollowIR: Evaluating and Teaching Information…](https://md.research.elamur.ai/free/en/papers/weller-2025-followir-evaluating-teaching-information-retrieval/) / cited by

# Who cites «FollowIR: Evaluating and Teaching Information Retrieval Models to Follow Instructions»

30 papers of our library cite it (of the 149,439 whose bibliographies we read) · 8 with a page here, the most cited first · [← back to «FollowIR: Evaluating and Teaching Information Retrieval Models to Follow Instructions»](https://md.research.elamur.ai/free/en/papers/weller-2025-followir-evaluating-teaching-information-retrieval/)

1. [**Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models arXiv:2506.05176**Yanzhao Zhang, Mingxin Li, Dingkun Long, et al.*2025 · 322 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2025-qwen3-embedding-advancing-text/)
2. [**MMTEB: Massive Multilingual Text Embedding Benchmark arXiv:2502.13595**Kenneth Enevoldsen, Isaac Chung, Imene Kerboua, et al.*2025 · 103 citations*](https://md.research.elamur.ai/free/en/papers/enevoldsen-2025-mmteb-massive-multilingual-text-embedding/)
3. [**BRIGHT: A Realistic and Challenging Benchmark for Reasoning-Intensive Retrieval arXiv:2407.12883**Hongjin Su, Howard Yen, Mengzhou Xia, et al.*2024 · 77 citations*](https://md.research.elamur.ai/free/en/papers/su-2024-bright-realistic-challenging-benchmark-reasoning/)
4. **[DRAMA: Diverse Augmentation from Large Language Models to Smaller Dense Retrievers](https://md.research.elamur.ai/free/en/papers/ma-2025-drama-diverse-augmentation-large-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xueguang Ma, Xi Victoria Lin, Barlas Oguz, et al.*2025 · 13 citations*
5. **[Diffusion vs. Autoregressive Language Models: A Text Embedding Perspective](https://md.research.elamur.ai/free/en/papers/zhang-2025-diffusion-vs-autoregressive-language-models/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Siyue Zhang, Yilun Zhao, Liyuan Geng, et al.*2025 · 10 citations*
6. **[Code-Switching Information Retrieval: Benchmarks, Analysis, and the Limits of Current Retrievers](https://md.research.elamur.ai/free/en/papers/zeng-2026-code-switching-information-retrieval-benchmarks/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Qingcheng Zeng, Yuheng Lu, Zeqi Zhou, et al.*2026 · 1 citation*
7. **[ToolDreamer: Instilling LLM Reasoning Into Tool Retrievers](https://md.research.elamur.ai/free/en/papers/sengupta-2026-tooldreamer-instilling-llm-reasoning-into/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Saptarshi Sengupta, Zhengyu Zhou, Jun Araki, et al.*2026 · 1 citation*
8. **[A Survey of Reasoning-Intensive Retrieval: Progress and Challenges](https://md.research.elamur.ai/free/en/papers/wei-2026-survey-reasoning-intensive-retrieval-progress/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yiyang Wei, Tingyu Song, Siyue Zhang, et al.*2026*

22 more papers of our library cite it; they have no page here yet.

- [← back to](https://md.research.elamur.ai/free/en/papers/weller-2025-followir-evaluating-teaching-information-retrieval/) — FollowIR: Evaluating and Teaching Information Retrieval Models to Follow Instructions