---
title: "Who cites Leave No Document Behind: — 57 citing papers"
description: "57 papers of the Elamur Research library cite «Leave No Document Behind: Benchmarking Long-Context LLMs with Extended Multi-Doc QA»…"
canonical: "https://research.elamur.ai/free/en/papers/wang-2024-leave-no-document-behind-benchmarking/cited-by/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/wang-2024-leave-no-document-behind-benchmarking/cited-by/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Leave No Document Behind: Benchmarking…](https://md.research.elamur.ai/free/en/papers/wang-2024-leave-no-document-behind-benchmarking/) / cited by

# Who cites «Leave No Document Behind: Benchmarking Long-Context LLMs with Extended Multi-Doc QA»

57 papers of our library cite it (of the 141,840 whose bibliographies we read) · 3 with a page here, the most cited first · [← back to «Leave No Document Behind: Benchmarking Long-Context LLMs with Extended Multi-Doc QA»](https://md.research.elamur.ai/free/en/papers/wang-2024-leave-no-document-behind-benchmarking/)

1. [**LongBench v2: Towards Deeper Understanding and Reasoning on Realistic Long-context Multitasks arXiv:2412.15204**Yushi Bai, Shangqing Tu, Jiajie Zhang, et al.*2024 · 130 citations*](https://md.research.elamur.ai/free/en/papers/bai-2024-longbench-v2-deeper-understanding/)
2. [**MagicPIG: LSH Sampling for Efficient LLM Generation arXiv:2410.16179**Zhuoming Chen, Ranajoy Sadhukhan, Zihao Ye, et al.*2024 · 66 citations*](https://md.research.elamur.ai/free/en/papers/chen-2024-magicpig-lsh-sampling-efficient-llm/)
3. [**A Comprehensive Survey on Long Context Language Modeling arXiv:2503.17407**Jiaheng Liu, Dawei Zhu, Zhiqi Bai, et al.*2025 · 64 citations*](https://md.research.elamur.ai/free/en/papers/liu-2025-comprehensive-survey-long-context-language/)

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

- [← back to](https://md.research.elamur.ai/free/en/papers/wang-2024-leave-no-document-behind-benchmarking/) — Leave No Document Behind: Benchmarking Long-Context LLMs with Extended Multi-Doc QA