---
title: "Who cites Code Execution with Pre-trained Language Models"
description: "12 papers of the Elamur Research library cite «Code Execution with Pre-trained Language Models», found in their bibliographies; the most…"
canonical: "https://research.elamur.ai/free/en/papers/liu-2023-code-execution-pre-trained-language/cited-by/"
---
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[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Code Execution with Pre-trained Language Models](https://md.research.elamur.ai/free/en/papers/liu-2023-code-execution-pre-trained-language/) / cited by

# Who cites «Code Execution with Pre-trained Language Models»

12 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 «Code Execution with Pre-trained Language Models»](https://md.research.elamur.ai/free/en/papers/liu-2023-code-execution-pre-trained-language/)

1. **[Can Language Models Solve Graph Problems in Natural Language?](https://md.research.elamur.ai/free/en/papers/wang-2023-language-models-solve-graph-problems/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Heng Wang, Shangbin Feng, Tianxing He, et al.*2023 · 124 citations*
2. [**CRUXEval: A Benchmark for Code Reasoning, Understanding and Execution arXiv:2401.03065**Alex Gu, Baptiste Rozière, Hugh Leather, et al.*2024 · 122 citations*](https://md.research.elamur.ai/free/en/papers/gu-2024-cruxeval-benchmark-code-reasoning-understanding/)
3. [**CodeI/O: Condensing Reasoning Patterns via Code Input-Output Prediction arXiv:2502.07316**Junlong Li, Daya Guo, Dejian Yang, et al.*2025 · 21 citations*](https://md.research.elamur.ai/free/en/papers/li-2025-codei-o-condensing-reasoning/)
4. **[EquiBench: Benchmarking Large Language Models' Reasoning about Program Semantics via Equivalence Checking](https://md.research.elamur.ai/free/en/papers/wei-2025-equibench-benchmarking-large-language-models/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Anjiang Wei, Jiannan Cao, Ran Li, et al.*2025 · 8 citations*
5. **[ExecVerify: White-Box RL with Verifiable Stepwise Rewards for Code Execution Reasoning](https://md.research.elamur.ai/free/en/papers/tang-2026-execverify-white-box-rl-verifiable/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Lingxiao Tang, He Ye, Zhaoyang Chu, et al.*2026 · 1 citation*
6. [**Should Code Models Learn Pedagogically? A Preliminary Evaluation of Curriculum Learning for Real-World Software Engineering Tasks arXiv:2502.03806**Kyi Shin Khant, Hong Yi Lin, Patanamon Thongtanunam*2025 · 1 citation*](https://md.research.elamur.ai/free/en/papers/khant-2025-should-code-models-learn-pedagogically/)

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

- [← back to](https://md.research.elamur.ai/free/en/papers/liu-2023-code-execution-pre-trained-language/) — Code Execution with Pre-trained Language Models