---
title: "Who cites John J. Nay — 105 papers of our library"
description: "105 papers of the Elamur Research library cite the works of John J. Nay, found in their bibliographies; the most cited first."
canonical: "https://research.elamur.ai/free/en/authors/john-j-nay/cited-by/"
---
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# Who cites John J. Nay

109 citations of their works from 105 papers of our library (of the 149,439 whose bibliographies we read) · 33 with a page here, the most cited first · [← John J. Nay](https://md.research.elamur.ai/free/en/authors/john-j-nay/)

1. **[A Survey on Evaluation of Large Language Models](https://md.research.elamur.ai/free/en/papers/chang-2023-survey-evaluation-large-language-models/) [ACM Transactions on Intelligent Systems and Technology](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-intelligent-systems-and-technology/)**Yupeng Chang, Xu Wang, Jindong Wang, et al.*2023 · 3,648 citations*
2. **[A Survey of Large Language Models](https://md.research.elamur.ai/free/en/papers/zhao-2023-survey-large-language-models/) [Frontiers of Computer Science](https://md.research.elamur.ai/free/en/venues/frontiers-of-computer-science/)**Wayne Xin Zhao, Kun Zhou, Junyi Li, et al.*2023 · 2,875 citations*
3. **[How Does NLP Benefit Legal System: A Summary of Legal Artificial Intelligence](https://md.research.elamur.ai/free/en/papers/zhong-2020-how-does-nlp-benefit-legal/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Haoxi Zhong, Chaojun Xiao, Cunchao Tu, et al.*2020 · 518 citations*
4. [**Large Legal Fictions: Profiling Legal Hallucinations in Large Language Models arXiv:2401.01301 · Journal of Legal Analysis 16, no. 1 (2024): 64-93**Matthew Dahl, Varun Magesh, Mirac Suzgun, et al.*2024 · 437 citations*](https://md.research.elamur.ai/free/en/papers/dahl-2024-large-legal-fictions-profiling-legal/)
5. [**LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Models arXiv:2308.11462**Neel Guha; Julian Nyarko; Daniel E. Ho; Christopher Re; Adam Chilton; Aditya Narayana; Alex Chohlas-Wood; Austin Peters; Brandon Waldon; Daniel N. Rockmore; Diego Zambrano; Dmitry Talisman; Enam Hoque; Faiz Surani; Frank Fagan; Galit Sarfaty; Gregory M. Dickinson; Haggai Porat; Jason Hegland; Jessica Wu; Joe Nudell; Joel Niklaus; John Nay; Jonathan H. Choi; Kevin Tobia; Margaret Hagan; Megan Ma; Michael Livermore; Nikon Rasumov-Rahe; Nils Holzenberger; Noam Kolt; Peter Henderson; Sean Rehaag; Sharad Goel; Shang Gao; Spencer Williams; Sunny Gandhi; Tom Zur; Varun Iyer;, Zehua Li*2023 · 389 citations*](https://md.research.elamur.ai/free/en/papers/guha-2023-legalbench-collaboratively-built-benchmark-measuring/)
6. **[Large Language Models for Mathematical Reasoning: Progresses and Challenges](https://md.research.elamur.ai/free/en/papers/ahn-2024-large-language-models-mathematical-reasoning/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Janice Ahn, Rishu Verma, Renze Lou, et al.*2024 · 280 citations*
7. [**TrustLLM: Trustworthiness in Large Language Models arXiv:2401.05561**Yue Huang, Lichao Sun, Haoran Wang, et al.*2024 · 237 citations*](https://md.research.elamur.ai/free/en/papers/huang-2024-trustllm-trustworthiness-large-language-models/)
8. [**LawBench: Benchmarking Legal Knowledge of Large Language Models arXiv:2309.16289**Zhiwei Fei, Xiaoyu Shen, Dawei Zhu, et al.*2023 · 188 citations*](https://md.research.elamur.ai/free/en/papers/fei-2023-lawbench-benchmarking-legal-knowledge-large/)
9. [**Detecting Language Model Attacks with Perplexity arXiv:2308.14132**Gabriel Alon, Michael Kamfonas*2023 · 141 citations*](https://md.research.elamur.ai/free/en/papers/alon-2023-detecting-language-model-attacks-perplexity/)
10. [**Platypus: Quick, Cheap, and Powerful Refinement of LLMs arXiv:2308.07317**Ariel N. Lee, Cole J. Hunter, Nataniel Ruiz*2023 · 117 citations*](https://md.research.elamur.ai/free/en/papers/lee-2023-platypus-quick-cheap-powerful-refinement/)
11. [**Prompt Cache: Modular Attention Reuse for Low-Latency Inference MLSys 2024**In Gim, Guojun Chen, Seung-seob Lee, et al.*2023 · 109 citations*](https://md.research.elamur.ai/free/en/papers/gim-2023-prompt-cache-modular-attention-reuse/)
12. **[GSM-Plus: A Comprehensive Benchmark for Evaluating the Robustness of LLMs as Mathematical Problem Solvers](https://md.research.elamur.ai/free/en/papers/li-2024-gsm-plus-comprehensive-benchmark-evaluating/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Qintong Li, Leyang Cui, Xueliang Zhao, et al.*2024 · 108 citations*
13. [**Nemotron-4 340B Technical Report arXiv:2406.11704**Nvidia: Bo Adler, Niket Agarwal, Ashwath Aithal, et al.*2024 · 107 citations*](https://md.research.elamur.ai/free/en/papers/adler-2024-nemotron-4-340b-technical-report/)
14. [**FrontierMath: A Benchmark for Evaluating Advanced Mathematical Reasoning in AI arXiv:2411.04872**Elliot Glazer, Ege Erdil, Tamay Besiroglu, et al.*2024 · 97 citations*](https://md.research.elamur.ai/free/en/papers/glazer-2024-frontiermath-benchmark-evaluating-advanced-mathematical/)
15. [**Foundational Challenges in Assuring Alignment and Safety of Large Language Models arXiv:2404.09932**Usman Anwar, Abulhair Saparov, Javier Rando, et al.*2024 · 87 citations*](https://md.research.elamur.ai/free/en/papers/anwar-2024-foundational-challenges-assuring-alignment-safety/)
16. [**PaperBench: Evaluating AI's Ability to Replicate AI Research arXiv:2504.01848**Giulio Starace, Oliver Jaffe, Dane Sherburn, et al.*2025 · 86 citations*](https://md.research.elamur.ai/free/en/papers/starace-2025-paperbench-evaluating-ai-s/)
17. **[Evaluating Mathematical Reasoning Beyond Accuracy](https://md.research.elamur.ai/free/en/papers/xia-2024-evaluating-mathematical-reasoning-accuracy/) [Proceedings of the AAAI Conference on Artificial Intelligence](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-aaai-conference-on-artificial-intelligence/)**Shijie Xia, Xuefeng Li, Yixin Liu, et al.*2024 · 85 citations*
18. [**HelpSteer2: Open-source dataset for training top-performing reward models arXiv:2406.08673**Zhilin Wang, Yi Dong, Olivier Delalleau, et al.*2024 · 82 citations*](https://md.research.elamur.ai/free/en/papers/wang-2024-helpsteer2-open-source-dataset-training/)
19. **[LexEval: A Comprehensive Chinese Legal Benchmark for Evaluating Large Language Models](https://md.research.elamur.ai/free/en/papers/li-2024-lexeval-comprehensive-chinese-legal-benchmark/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Haitao Li, You Chen, Qingyao Ai, et al.*2024 · 77 citations*
20. **[KMMLU: Measuring Massive Multitask Language Understanding in Korean](https://md.research.elamur.ai/free/en/papers/son-2025-kmmlu-measuring-massive-multitask-language/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Guijin Son, Hanwool Lee, Sungdong Kim, et al.*2025 · 76 citations*
21. [**Teaching Large Language Models to Reason with Reinforcement Learning arXiv:2403.04642**Alex Havrilla, Yuqing Du, Sharath Chandra Raparthy, et al.*2024 · 61 citations*](https://md.research.elamur.ai/free/en/papers/havrilla-2024-teaching-large-language-models-reason/)
22. [**Dialect prejudice predicts AI decisions about people's character, employability, and criminality arXiv:2403.00742**Valentin Hofmann, Pratyusha Ria Kalluri, Dan Jurafsky, et al.*2024 · 57 citations*](https://md.research.elamur.ai/free/en/papers/hofmann-2024-dialect-prejudice-predicts-ai-decisions/)
23. [**CMB: A Comprehensive Medical Benchmark in Chinese arXiv:2308.08833**Xidong Wang, Guiming Hardy Chen, Dingjie Song, et al.*2023 · 54 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-cmb-comprehensive-medical-benchmark/)
24. [**GLoRe: When, Where, and How to Improve LLM Reasoning via Global and Local Refinements arXiv:2402.10963**Alex Havrilla, Sharath Raparthy, Christoforus Nalmpantis, et al.*2024 · 45 citations*](https://md.research.elamur.ai/free/en/papers/havrilla-2024-glore-where-improve-llm-reasoning/)
25. **[ArabLegalEval: A Multitask Benchmark for Assessing Arabic Legal Knowledge in Large Language Models](https://md.research.elamur.ai/free/en/papers/hijazi-2024-arablegaleval-multitask-benchmark-assessing-arabic/) [Arabic Natural Language Processing Conference (ArabicNLP)](https://md.research.elamur.ai/free/en/venues/arabicnlp/)**Faris Hijazi, Somayah AlHarbi, Abdulaziz AlHussein, et al.*2024 · 28 citations*
26. [**GameBench: Evaluating Strategic Reasoning Abilities of LLM Agents arXiv:2406.06613**Anthony Costarelli, Mat Allen, Roman Hauksson, et al.*2024 · 20 citations*](https://md.research.elamur.ai/free/en/papers/costarelli-2024-gamebench-evaluating-strategic-reasoning-abilities/)
27. **[RCBSF: A Multi-Agent Framework for Automated Contract Revision via Stackelberg Game](https://md.research.elamur.ai/free/en/papers/xu-2026-rcbsf-multi-agent-framework-automated/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Shijia Xu, Yu Wang, Xiaolong Jia, et al.*2026 · 11 citations*
28. **[Exposing the Achilles' Heel: Evaluating LLMs Ability to Handle Mistakes in Mathematical Reasoning](https://md.research.elamur.ai/free/en/papers/singh-2025-exposing-achilles-heel-evaluating-llms/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Joykirat Singh, Akshay Nambi, Vibhav Vineet*2025 · 8 citations*
29. [**Cutting Through the Noise: Boosting LLM Performance on Math Word Problems**Ujjwala Anantheswaran, Himanshu Gupta, Kevin Scaria, et al.*2024 · 7 citations*](https://md.research.elamur.ai/free/en/papers/anantheswaran-2024-cutting-through-noise-boosting-llm/)
30. **[The Validation Gap: A Mechanistic Analysis of How Language Models Compute Arithmetic but Fail to Validate It](https://md.research.elamur.ai/free/en/papers/bertolazzi-2025-validation-gap-mechanistic-analysis-how/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Leonardo Bertolazzi, Philipp Mondorf, Barbara Plank, et al.*2025 · 6 citations*
31. **[From Benchmarks to Skills: Low-Rank Factors for LLM Evaluation](https://md.research.elamur.ai/free/en/papers/maimon-2026-benchmarks-skills-low-rank-factors/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Aviya Maimon, Amir David Nisan Cohen, Gal Vishne, et al.*2026*
32. **[PyraMathBench: Evaluating and Improving Mathematical Capability in Large Language Models](https://md.research.elamur.ai/free/en/papers/ouyang-2026-pyramathbench-evaluating-improving-mathematical-capability/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zetian Ouyang, Linlin Wang, Gerard de Melo, et al.*2026*
33. **[R ESEARCH QA: Evaluating Scholarly Question Answering at Scale Across 75 Fields with Survey-Mined Questions and Rubrics](https://md.research.elamur.ai/free/en/papers/yifei-2026-r-esearch-qa-evaluating-scholarly/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Li S. Yifei, Allen Chang, Chaitanya Malaviya, et al.*2026*

72 more citing papers of our library have no page here yet.

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