---
title: "Who cites Ryan-Rhys Griffiths — 149 papers of our library"
description: "149 papers of the Elamur Research library cite the works of Ryan-Rhys Griffiths, found in their bibliographies; the most cited first."
canonical: "https://research.elamur.ai/free/en/authors/ryan-rhys-griffiths/cited-by/"
---
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# Who cites Ryan-Rhys Griffiths

150 citations of their works from 149 papers of our library (of the 149,439 whose bibliographies we read) · 59 with a page here, the most cited first · [← Ryan-Rhys Griffiths](https://md.research.elamur.ai/free/en/authors/ryan-rhys-griffiths/)

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. [**Summary of ChatGPT-Related Research and Perspective Towards the Future of Large Language Models arXiv:2304.01852 · Meta-Radiology (2023)100,017**Yiheng Liu, Tianle Han, Siyuan Ma, et al.*2023 · 940 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-summary-chatgpt-related-research-perspective/)
3. [**A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT arXiv:2302.11382**Jules White, Quchen Fu, Sam Hays, et al.*2023 · 904 citations*](https://md.research.elamur.ai/free/en/papers/white-2023-prompt-pattern-catalog-enhance-prompt/)
4. [**A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity arXiv:2302.04023**Yejin Bang, Samuel Cahyawijaya, Nayeon Lee, et al.*2023 · 882 citations*](https://md.research.elamur.ai/free/en/papers/bang-2023-multitask-multilingual-multimodal-evaluation-chatgpt/)
5. [**A Categorical Archive of ChatGPT Failures arXiv:2302.03494**Ali Borji*2023 · 559 citations*](https://md.research.elamur.ai/free/en/papers/borji-2023-categorical-archive-chatgpt-failures/)
6. [**MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts arXiv:2310.02255**Pan Lu, Hritik Bansal, Tony Xia, et al.*2023 · 468 citations*](https://md.research.elamur.ai/free/en/papers/lu-2023-mathvista-evaluating-mathematical-reasoning-foundation/)
7. **[OlympiadBench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problems](https://md.research.elamur.ai/free/en/papers/he-2024-olympiadbench-challenging-benchmark-promoting-agi/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Chaoqun He, Renjie Luo, Yuzhuo Bai, et al.*2024 · 456 citations*
8. **[AugGPT: Leveraging ChatGPT for Text Data Augmentation](https://md.research.elamur.ai/free/en/papers/dai-2023-auggpt-leveraging-chatgpt-text-data/) [IEEE Transactions on Big Data](https://md.research.elamur.ai/free/en/venues/ieee-transactions-on-big-data/)**Haixing Dai; Zhengliang Liu; Wenxiong Liao; Xiaoke Huang; Yihan Cao; Zihao Wu; Lin Zhao; Shaochen Xu; Wei Liu; Ninghao Liu; Sheng Li; Dajiang Zhu; Hongmin Cai; Lichao Sun; Quanzheng Li; Dinggang Shen; Tianming Liu;, Xiang Li*2023 · 306 citations*
9. **[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*
10. [**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/)
11. [**Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment arXiv:2308.05374**Yang Liu; Yuanshun Yao; Jean-Francois Ton; Xiaoying Zhang; Ruocheng Guo; Hao Cheng; Yegor Klochkov; Muhammad Faaiz Taufiq;, Hang Li*2023 · 223 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-trustworthy-llms-survey-guideline-evaluating/)
12. **[A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark Datasets](https://md.research.elamur.ai/free/en/papers/laskar-2023-systematic-study-comprehensive-evaluation-chatgpt/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Md Tahmid Rahman Laskar, M Saiful Bari, Mizanur Rahman, et al.*2023 · 200 citations*
13. **[A Survey of Safety and Trustworthiness of Large Language Models through the Lens of Verification and Validation](https://md.research.elamur.ai/free/en/papers/huang-2023-survey-safety-trustworthiness-large-language/) [Artificial Intelligence Review](https://md.research.elamur.ai/free/en/venues/artificial-intelligence-review/)**Xiaowei Huang, Wenjie Ruan, Wei Huang, et al.*2023 · 199 citations*
14. **[How does GPT-2 compute greater-than?: Interpreting mathematical abilities in a pre-trained language model](https://md.research.elamur.ai/free/en/papers/hanna-2023-gpt-2-compute-greater-than/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Michael Hanna, Ollie Liu, Alexandre Variengien*2023 · 151 citations*
15. [**SciBench: Evaluating College-Level Scientific Problem-Solving Abilities of Large Language Models arXiv:2307.10635**Xiaoxuan Wang, Ziniu Hu, Pan Lu, et al.*2023 · 149 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-scibench-evaluating-college-level-scientific/)
16. [**Evaluating ChatGPT's Information Extraction Capabilities: An Assessment of Performance, Explainability, Calibration, and Faithfulness arXiv:2304.11633**Bo Li, Gexiang Fang, Yang Yang, et al.*2023 · 142 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-evaluating-chatgpt-s-information-extraction/)
17. **[MathDial: A Dialogue Tutoring Dataset with Rich Pedagogical Properties Grounded in Math Reasoning Problems](https://md.research.elamur.ai/free/en/papers/macina-2023-mathdial-dialogue-tutoring-dataset-rich/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jakub Macina, Nico Daheim, Sankalan Chowdhury, et al.*2023 · 131 citations*
18. [**Progressive-Hint Prompting Improves Reasoning in Large Language Models arXiv:2304.09797**Chuanyang Zheng, Zhengying Liu, Enze Xie, et al.*2023 · 131 citations*](https://md.research.elamur.ai/free/en/papers/zheng-2023-progressive-hint-prompting-improves-reasoning/)
19. [**A comprehensive evaluation of ChatGPT's zero-shot Text-to-SQL capability arXiv:2303.13547**Aiwei Liu, Xuming Hu, Lijie Wen, et al.*2023 · 127 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-comprehensive-evaluation-chatgpt-s-zero/)
20. [**ChatGPT or Grammarly? Evaluating ChatGPT on Grammatical Error Correction Benchmark arXiv:2303.13648**Haoran Wu, Wenxuan Wang, Yuxuan Wan, et al.*2023 · 118 citations*](https://md.research.elamur.ai/free/en/papers/wu-2023-chatgpt-grammarly-evaluating-chatgpt-grammatical/)
21. **[Can we trust the evaluation on ChatGPT?](https://md.research.elamur.ai/free/en/papers/aiyappa-2023-can-we-trust-evaluation-chatgpt/) [Workshop on Trustworthy NLP](https://md.research.elamur.ai/free/en/venues/trustnlp/)**Rachith Aiyappa, Jisun An, Haewoon Kwak, et al.*2023 · 103 citations*
22. **[MathArena: Evaluating LLMs on Uncontaminated Math Competitions](https://md.research.elamur.ai/free/en/papers/balunovic-2025-matharena-evaluating-llms-uncontaminated-math/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Mislav Balunović, Jasper Dekoninck, Ivo Petrov, et al.*2025 · 99 citations*
23. **[FELM: Benchmarking Factuality Evaluation of Large Language Models](https://md.research.elamur.ai/free/en/papers/chen-2023-felm-benchmarking-factuality-evaluation-large/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Shiqi Chen, Yiran Zhao, Jinghan Zhang, et al.*2023 · 99 citations*
24. [**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/)
25. [**How well do Large Language Models perform in Arithmetic tasks? arXiv:2304.02015**Zheng Yuan, Hongyi Yuan, Chuanqi Tan, et al.*2023 · 93 citations*](https://md.research.elamur.ai/free/en/papers/yuan-2023-well-large-language-models-perform/)
26. **[Exploring the Potential of ChatGPT on Sentence Level Relations: A Focus on Temporal, Causal, and Discourse Relations](https://md.research.elamur.ai/free/en/papers/chan-2024-exploring-potential-chatgpt-sentence-level/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Chunkit Chan, Cheng Jiayang, Weiqi Wang, et al.*2024 · 92 citations*
27. **[GPTEval: A Survey on Assessments of ChatGPT and GPT-4](https://md.research.elamur.ai/free/en/papers/mao-2024-gpteval-survey-assessments-chatgpt-gpt/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Rui Mao, Guanyi Chen, Xulang Zhang, et al.*2024 · 75 citations*
28. **[Self-Contrast: Better Reflection Through Inconsistent Solving Perspectives](https://md.research.elamur.ai/free/en/papers/zhang-2024-self-contrast-better-reflection-through/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Wenqi Zhang, Yongliang Shen, Linjuan Wu, et al.*2024 · 75 citations*
29. [**INCLUDE: Evaluating Multilingual Language Understanding with Regional Knowledge arXiv:2411.19799**Angelika Romanou, Negar Foroutan, Anna Sotnikova, et al.*2024 · 74 citations*](https://md.research.elamur.ai/free/en/papers/romanou-2024-include-evaluating-multilingual-language-understanding/)
30. [**Evaluation of OpenAI o1: Opportunities and Challenges of AGI arXiv:2409.18486**Tianyang Zhong, Zhengliang Liu, Yi Pan, et al.*2024 · 64 citations*](https://md.research.elamur.ai/free/en/papers/zhong-2024-evaluation-openai-o1-opportunities-challenges/)
31. [**Formal Mathematical Reasoning: A New Frontier in AI arXiv:2412.16075**Kaiyu Yang, Gabriel Poesia, Jingxuan He, et al.*2024 · 49 citations*](https://md.research.elamur.ai/free/en/papers/yang-2024-formal-mathematical-reasoning-new-frontier/)
32. [**Alice in Wonderland: Simple Tasks Showing Complete Reasoning Breakdown in State-Of-the-Art Large Language Models arXiv:2406.02061**Marianna Nezhurina, Lucia Cipolina-Kun, Mehdi Cherti, et al.*2024 · 48 citations*](https://md.research.elamur.ai/free/en/papers/nezhurina-2024-alice-wonderland-simple-tasks-showing/)
33. **[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*
34. [**A Survey on Deep Learning for Theorem Proving arXiv:2404.09939**Zhaoyu Li, Jialiang Sun, Logan Murphy, et al.*2024 · 46 citations*](https://md.research.elamur.ai/free/en/papers/li-2024-survey-deep-learning-theorem-proving/)
35. **[MathOdyssey: Benchmarking Mathematical Problem-Solving Skills in Large Language Models Using Odyssey Math Data](https://md.research.elamur.ai/free/en/papers/fang-2024-mathodyssey-benchmarking-mathematical-problem-solving/) [Scientific Data](https://md.research.elamur.ai/free/en/venues/scientific-data/)**Meng Fang, Xiangpeng Wan, Fei Lu, et al.*2024 · 46 citations*
36. [**Towards AI-Assisted Synthesis of Verified Dafny Methods arXiv:2402.00247**Md Rakib Hossain Misu, Cristina V. Lopes, Iris Ma, et al.*2024 · 46 citations*](https://md.research.elamur.ai/free/en/papers/misu-2024-ai-assisted-synthesis-verified-dafny/)
37. [**When does In-context Learning Fall Short and Why? A Study on Specification-Heavy Tasks arXiv:2311.08993**Hao Peng, Xiaozhi Wang, Jianhui Chen, et al.*2023 · 42 citations*](https://md.research.elamur.ai/free/en/papers/peng-2023-context-learning-fall-short-why/)
38. [**DynaMath: A Dynamic Visual Benchmark for Evaluating Mathematical Reasoning Robustness of Vision Language Models arXiv:2411.00836**Chengke Zou, Xingang Guo, Rui Yang, et al.*2024 · 41 citations*](https://md.research.elamur.ai/free/en/papers/zou-2024-dynamath-dynamic-visual-benchmark-evaluating/)
39. [**Evaluating Language Models for Mathematics through Interactions arXiv:2306.01694**Katherine M. Collins, Albert Q. Jiang, Simon Frieder, et al.*2023 · 40 citations*](https://md.research.elamur.ai/free/en/papers/collins-2023-evaluating-language-models-mathematics-through/)
40. [**Let's reward step by step: Step-Level reward model as the Navigators for Reasoning arXiv:2310.10080**Qianli Ma, Haotian Zhou, Tingkai Liu, et al.*2023 · 40 citations*](https://md.research.elamur.ai/free/en/papers/ma-2023-let-s-reward-step-step/)
41. **[PaD: Program-aided Distillation Can Teach Small Models Reasoning Better than Chain-of-thought Fine-tuning](https://md.research.elamur.ai/free/en/papers/zhu-2024-pad-program-aided-distillation-can/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Xuekai Zhu, Biqing Qi, Kaiyan Zhang, et al.*2024 · 35 citations*
42. [**Proof or Bluff? Evaluating LLMs on 2025 USA Math Olympiad arXiv:2503.21934**Ivo Petrov, Jasper Dekoninck, Lyuben Baltadzhiev, et al.*2025 · 31 citations*](https://md.research.elamur.ai/free/en/papers/petrov-2025-proof-or-bluff-evaluating-llms/)
43. [**Language Models as Science Tutors arXiv:2402.11111**Alexis Chevalier, Jiayi Geng, Alexander Wettig, et al.*2024 · 30 citations*](https://md.research.elamur.ai/free/en/papers/chevalier-2024-language-models-science-tutors/)
44. [**Taqyim: Evaluating Arabic NLP Tasks Using ChatGPT Models arXiv:2306.16322**Zaid Alyafeai, Maged S. Alshaibani, Badr AlKhamissi, et al.*2023 · 25 citations*](https://md.research.elamur.ai/free/en/papers/alyafeai-2023-taqyim-evaluating-arabic-nlp-tasks/)
45. [**Robin: A multi-agent system for automating scientific discovery arXiv:2505.13400**Ali Essam Ghareeb; Benjamin Chang; Ludovico Mitchener; Angela Yiu; Caralyn J. Szostkiewicz; Jon M. Laurent; Muhammed T. Razzak; Andrew D. White; Michaela M. Hinks;, Samuel G. Rodriques*2025 · 22 citations*](https://md.research.elamur.ai/free/en/papers/ghareeb-2025-robin-multi-agent-system/)
46. **[AI4Research: A Survey of Artificial Intelligence for Scientific Research](https://md.research.elamur.ai/free/en/papers/chen-2025-ai4research-survey-artificial-intelligence/) [Science China Information Sciences](https://md.research.elamur.ai/free/en/venues/science-china-information-sciences/)**Qiguang Chen, Mingda Yang, Libo Qin, et al.*2025 · 21 citations*
47. [**xVal: A Continuous Numerical Tokenization for Scientific Language Models arXiv:2310.02989**Siavash Golkar, Mariel Pettee, Michael Eickenberg, et al.*2023 · 19 citations*](https://md.research.elamur.ai/free/en/papers/golkar-2023-xval-continuous-numerical-tokenization-scientific/)
48. [**Aviary: training language agents on challenging scientific tasks arXiv:2412.21154**Siddharth Narayanan, James D. Braza, Ryan-Rhys Griffiths, et al.*2024 · 18 citations*](https://md.research.elamur.ai/free/en/papers/narayanan-2024-aviary-training-language-agents/)
49. [**EXP-Bench: Can AI Conduct AI Research Experiments? arXiv:2505.24785**Patrick Tser Jern Kon, Jiachen Liu, Xinyi Zhu, et al.*2025 · 17 citations*](https://md.research.elamur.ai/free/en/papers/kon-2025-exp-bench-ai-conduct/)
50. **[STaR-SQL: Self-Taught Reasoner for Text-to-SQL](https://md.research.elamur.ai/free/en/papers/he-2025-star-sql-self-taught-reasoner/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mingqian He, Yongliang Shen, Wenqi Zhang, et al.*2025 · 17 citations*
51. [**Training a Scientific Reasoning Model for Chemistry arXiv:2506.17238**Siddharth M. Narayanan, James D. Braza, Ryan-Rhys Griffiths, et al.*2025 · 15 citations*](https://md.research.elamur.ai/free/en/papers/narayanan-2025-training-scientific-reasoning-model/)
52. **[A Symbolic Framework for Evaluating Mathematical Reasoning and Generalisation with Transformers](https://md.research.elamur.ai/free/en/papers/meadows-2024-symbolic-framework-evaluating-mathematical-reasoning/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Jordan Meadows, Marco Valentino, Damien Teney, et al.*2024 · 14 citations*
53. **[CMM-Math: A Chinese Multimodal Math Dataset To Evaluate and Enhance the Mathematics Reasoning of Large Multimodal Models](https://md.research.elamur.ai/free/en/papers/liu-2024-cmm-math-chinese-multimodal-math/) [ACM International Conference on Multimedia (ACM MM)](https://md.research.elamur.ai/free/en/venues/acm-international-conference-on-multimedia-acm-mm/)**Wentao Liu, Qianjun Pan, Yi Zhang, et al.*2024 · 13 citations*
54. [**No Train Still Gain. Unleash Mathematical Reasoning of Large Language Models with Monte Carlo Tree Search Guided by Energy Function arXiv:2309.03224**Haotian Xu*2023 · 12 citations*](https://md.research.elamur.ai/free/en/papers/xu-2023-no-train-still-gain-unleash/)
55. [**The Open Proof Corpus: A Large-Scale Study of LLM-Generated Mathematical Proofs arXiv:2506.21621**Jasper Dekoninck, Ivo Petrov, Kristian Minchev, et al.*2025 · 11 citations*](https://md.research.elamur.ai/free/en/papers/dekoninck-2025-open-proof-corpus-large-scale/)
56. [**Brains vs. Bytes: Evaluating LLM Proficiency in Olympiad Mathematics arXiv:2504.01995**Hamed Mahdavi, Alireza Hashemi, Majid Daliri, et al.*2025 · 8 citations*](https://md.research.elamur.ai/free/en/papers/mahdavi-2025-brains-vs-bytes-evaluating-llm/)
57. [**Mathematical Reasoning in Large Language Models: Assessing Logical and Arithmetic Errors across Wide Numerical Ranges arXiv:2502.08680**Safal Shrestha, Minwu Kim, Keith Ross*2025 · 6 citations*](https://md.research.elamur.ai/free/en/papers/shrestha-2025-mathematical-reasoning-large-language-models/)
58. **[A Survey on LLM-based Conversational User Simulation](https://md.research.elamur.ai/free/en/papers/ni-2026-survey-llm-based-conversational-user/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Bo Ni, Yu Wang, Leyao Wang, et al.*2026 · 1 citation*
59. **[ChemVLR: Prioritizing Reasoning in Perception for Chemical Vision-Language Understanding](https://md.research.elamur.ai/free/en/papers/zhao-2026-chemvlr-prioritizing-reasoning-perception-chemical/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xuanle Zhao, Xinyuan Cai, Xiang Cheng, et al.*2026*

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

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