--- title: "Who cites LV-Eval: A Balanced — 40 citing papers" description: "40 papers of the Elamur Research library cite «LV-Eval: A Balanced Long-Context Benchmark with 5 Length Levels Up to 256K», found in their…" canonical: "https://research.elamur.ai/free/en/papers/yuan-2024-lv-eval-balanced-long-context/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/yuan-2024-lv-eval-balanced-long-context/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [LV-Eval: A Balanced Long-Context Benchmark with 5…](https://md.research.elamur.ai/free/en/papers/yuan-2024-lv-eval-balanced-long-context/) / cited by # Who cites «LV-Eval: A Balanced Long-Context Benchmark with 5 Length Levels Up to 256K» 40 papers of our library cite it (of the 149,439 whose bibliographies we read) · 11 with a page here, the most cited first · [← back to «LV-Eval: A Balanced Long-Context Benchmark with 5 Length Levels Up to 256K»](https://md.research.elamur.ai/free/en/papers/yuan-2024-lv-eval-balanced-long-context/) 1. [**RULER: What's the Real Context Size of Your Long-Context Language Models? arXiv:2404.06654**Cheng-Ping Hsieh, Simeng Sun, Samuel Kriman, et al.*2024 · 400 citations*](https://md.research.elamur.ai/free/en/papers/hsieh-2024-ruler-s-real-context-size/) 2. [**Qwen2.5-1M Technical Report arXiv:2501.15383**An Yang, Bowen Yu, Chengyuan Li, et al.*2025 · 102 citations*](https://md.research.elamur.ai/free/en/papers/yang-2025-qwen2-5-1m-technical/) 3. **[LM-Infinite: Zero-Shot Extreme Length Generalization for Large Language Models](https://md.research.elamur.ai/free/en/papers/han-2024-lm-infinite-zero-shot-extreme/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Chi Han, Qifan Wang, Hao Peng, et al.*2024 · 95 citations* 4. [**InftyThink: Breaking the Length Limits of Long-Context Reasoning in Large Language Models arXiv:2503.06692**Yuchen Yan, Yongliang Shen, Yang Liu, et al.*2025 · 29 citations*](https://md.research.elamur.ai/free/en/papers/yan-2025-inftythink-breaking-length-limits-long/) 5. [**Hunyuan-Large: An Open-Source MoE Model with 52 Billion Activated Parameters by Tencent arXiv:2411.02265**Xingwu Sun, Yanfeng Chen, Yiqing Huang, et al.*2024 · 25 citations*](https://md.research.elamur.ai/free/en/papers/sun-2024-hunyuan-large-open-source/) 6. **[Counting-Stars: A Multi-evidence, Position-aware, and Scalable Benchmark for Evaluating Long-Context Large Language Models](https://md.research.elamur.ai/free/en/papers/song-2025-counting-stars-multi-evidence-position/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Mingyang Song, Mao Zheng, Xuan Luo*2025 · 20 citations* 7. **[LoGU: Long-form Generation with Uncertainty Expressions](https://md.research.elamur.ai/free/en/papers/yang-2025-logu-long-form-generation-uncertainty/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ruihan Yang, Caiqi Zhang, Zhisong Zhang, et al.*2025 · 13 citations* 8. **[Evaluating Multilingual Long-Context Models for Retrieval and Reasoning](https://md.research.elamur.ai/free/en/papers/agrawal-2024-evaluating-multilingual-long-context-models/) [Workshop on Multilingual Representation Learning](https://md.research.elamur.ai/free/en/venues/mrl/)**Ameeta Agrawal, Andy Dang, Sina Bagheri Nezhad, et al.*2024 · 13 citations* 9. **[From 128K to 4M: Efficient Training of Ultra-Long Context Large Language Models](https://md.research.elamur.ai/free/en/papers/xu-2026-128k-4m-efficient-training-ultra/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Chejian Xu, Wei Ping, Peng Xu, et al.*2026 · 5 citations* 10. **[Data Foundations of Long-Context Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/sun-2026-data-foundations-long-context-language/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Zechen Sun, Yuyang Sun, Zhaochen Su, et al.*2026* 11. **[RoBSA: RoPE-based Blockwise Sparse Multi-head Latent Attention](https://md.research.elamur.ai/free/en/papers/shi-2026-robsa-rope-based-blockwise-sparse/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xinyu Shi, Kairong Luo, Zhen Zheng, et al.*2026* 29 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/yuan-2024-lv-eval-balanced-long-context/) — LV-Eval: A Balanced Long-Context Benchmark with 5 Length Levels Up to 256K