---
title: "Who cites How predictable is language — 16 citing papers"
description: "16 papers of the Elamur Research library cite «How predictable is language model benchmark performance?», found in their bibliographies…"
canonical: "https://research.elamur.ai/free/en/papers/owen-2024-predictable-language-model-benchmark-performance/cited-by/"
---
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# Who cites «How predictable is language model benchmark performance?»

16 papers of our library cite it (of the 149,439 whose bibliographies we read) · 6 with a page here, the most cited first · [← back to «How predictable is language model benchmark performance?»](https://md.research.elamur.ai/free/en/papers/owen-2024-predictable-language-model-benchmark-performance/)

1. **[Humanity's Last Exam](https://md.research.elamur.ai/free/en/papers/phan-2025-humanity-s-last-exam/) [Nature](https://md.research.elamur.ai/free/en/venues/nature/)**Long Phan, Alice Gatti, Ziwen Han, et al.*2025 · 256 citations*
2. [**Seed1.5-VL Technical Report arXiv:2505.07062**Dong Guo, Faming Wu, Feida Zhu, et al.*2025 · 67 citations*](https://md.research.elamur.ai/free/en/papers/guo-2025-seed1-5-vl-technical/)
3. [**Observational Scaling Laws and the Predictability of Language Model Performance arXiv:2405.10938**Yangjun Ruan, Chris J. Maddison, Tatsunori Hashimoto*2024 · 40 citations*](https://md.research.elamur.ai/free/en/papers/ruan-2024-observational-scaling-laws-predictability-language/)
4. **[Measuring AI Ability to Complete Long Software Tasks](https://md.research.elamur.ai/free/en/papers/kwa-2025-measuring-ai-ability-complete/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Thomas Kwa, Ben West, Joel Becker, et al.*2025 · 36 citations*
5. [**The Art of Scaling Reinforcement Learning Compute for LLMs arXiv:2510.13786**Devvrit Khatri, Lovish Madaan, Rishabh Tiwari, et al.*2025 · 20 citations*](https://md.research.elamur.ai/free/en/papers/khatri-2025-art-scaling-reinforcement-learning-compute/)
6. **[AutoTaskEval: Towards Domain-Specific and Fine-Grained Evaluation for LLMs](https://md.research.elamur.ai/free/en/papers/lyu-2026-autotaskeval-domain-specific-fine-grained/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Qingqing Lyu, Linjuan Wu, Yongliang Shen, et al.*2026*

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

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