---
title: "Who cites Pretraining on the Test Set Is All — 20 citing papers"
description: "20 papers of the Elamur Research library cite «Pretraining on the Test Set Is All You Need», found in their bibliographies; the most cited first."
canonical: "https://research.elamur.ai/free/en/papers/schaeffer-2023-pretraining-test-set-all-need/cited-by/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/schaeffer-2023-pretraining-test-set-all-need/cited-by/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Pretraining on the Test Set Is All You Need](https://md.research.elamur.ai/free/en/papers/schaeffer-2023-pretraining-test-set-all-need/) / cited by

# Who cites «Pretraining on the Test Set Is All You Need»

20 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 «Pretraining on the Test Set Is All You Need»](https://md.research.elamur.ai/free/en/papers/schaeffer-2023-pretraining-test-set-all-need/)

1. [**LawBench: Benchmarking Legal Knowledge of Large Language Models arXiv:2309.16289**Zhiwei Fei, Xiaoyu Shen, Dawei Zhu, et al.*2023 · 186 citations*](https://md.research.elamur.ai/free/en/papers/fei-2023-lawbench-benchmarking-legal-knowledge-large/)
2. [**Chameleon: Mixed-Modal Early-Fusion Foundation Models arXiv:2405.09818**Chameleon Team*2024 · 159 citations*](https://md.research.elamur.ai/free/en/papers/team-2024-chameleon-mixed-modal-early-fusion/)
3. **[Investigating Data Contamination in Modern Benchmarks for Large Language Models](https://md.research.elamur.ai/free/en/papers/deng-2024-investigating-data-contamination-modern-benchmarks/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Chunyuan Deng, Yilun Zhao, Xiangru Tang, et al.*2024 · 137 citations*
4. **[NPHardEval: Dynamic Benchmark on Reasoning Ability of Large Language Models via Complexity Classes](https://md.research.elamur.ai/free/en/papers/fan-2024-nphardeval-dynamic-benchmark-reasoning-ability/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Lizhou Fan, Wenyue Hua, Lingyao Li, et al.*2024 · 47 citations*
5. [**Vibe-Eval: A hard evaluation suite for measuring progress of multimodal language models arXiv:2405.02287**Piotr Padlewski, Max Bain, Matthew Henderson, et al.*2024 · 19 citations*](https://md.research.elamur.ai/free/en/papers/padlewski-2024-vibe-eval-hard-evaluation-suite/)
6. **[JudgeAgent: Beyond Static Benchmarks for Knowledge-Driven and Dynamic LLM Evaluation](https://md.research.elamur.ai/free/en/papers/shi-2026-judgeagent-beyond-static-benchmarks-knowledge/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhichao Shi, Xuhui Jiang, Chengjin Xu, et al.*2026*

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

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