---
title: "Who cites Brando Miranda — 177 papers of our library"
description: "177 papers of the Elamur Research library cite the works of Brando Miranda, found in their bibliographies; the most cited first."
canonical: "https://research.elamur.ai/free/en/authors/brando-miranda/cited-by/"
---
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# Who cites Brando Miranda

177 citations of their works from 177 papers of our library (of the 149,439 whose bibliographies we read) · 49 with a page here, the most cited first · [← Brando Miranda](https://md.research.elamur.ai/free/en/authors/brando-miranda/)

1. **[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*
2. [**Yi: Open Foundation Models by 01.AI arXiv:2403.04652**Alex Young, Bei Chen, Chao Li, et al.*2024 · 408 citations*](https://md.research.elamur.ai/free/en/papers/ai-2024-yi-open-foundation-models-01/)
3. [**Challenges and Applications of Large Language Models arXiv:2307.10169**Jean Kaddour, Joshua Harris, Maximilian Mozes, et al.*2023 · 301 citations*](https://md.research.elamur.ai/free/en/papers/kaddour-2023-challenges-applications-large-language-models/)
4. [**GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models arXiv:2410.05229**Iman Mirzadeh, Keivan Alizadeh, Hooman Shahrokhi, et al.*2024 · 273 citations*](https://md.research.elamur.ai/free/en/papers/mirzadeh-2024-gsm-symbolic-understanding-limitations-mathematical/)
5. **[DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models](https://md.research.elamur.ai/free/en/papers/wang-2023-decodingtrust-comprehensive-assessment-trustworthiness-gpt/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Boxin Wang, Weixin Chen, Hengzhi Pei, et al.*2023 · 259 citations*
6. [**DeepSeek-VL: Towards Real-World Vision-Language Understanding arXiv:2403.05525**Haoyu Lu, Wen Liu, Bo Zhang, et al.*2024 · 208 citations*](https://md.research.elamur.ai/free/en/papers/lu-2024-deepseek-vl-real-world/)
7. [**Language models show human-like content effects on reasoning tasks arXiv:2207.07051**Ishita Dasgupta, Andrew K. Lampinen, Stephanie C. Y. Chan, et al.*2022 · 162 citations*](https://md.research.elamur.ai/free/en/papers/dasgupta-2022-language-models-show-human-like/)
8. [**Olmo 3 arXiv:2512.13961**Team Olmo: Allyson Ettinger, Amanda Bertsch, Bailey Kuehl, et al.*2025 · 130 citations*](https://md.research.elamur.ai/free/en/papers/ettinger-2025-olmo-3/)
9. [**Navigate through Enigmatic Labyrinth A Survey of Chain of Thought Reasoning: Advances, Frontiers and Future arXiv:2309.15402**Zheng Chu, Jingchang Chen, Qianglong Chen, et al.*2023 · 125 citations*](https://md.research.elamur.ai/free/en/papers/chu-2023-navigate-through-enigmatic-labyrinth-survey/)
10. [**The Alignment Problem from a Deep Learning Perspective arXiv:2209.00626**Richard Ngo, Lawrence Chan, Soren Mindermann*2022 · 121 citations*](https://md.research.elamur.ai/free/en/papers/ngo-2022-alignment-problem-deep-learning-perspective/)
11. **[The Impact of Reasoning Step Length on Large Language Models](https://md.research.elamur.ai/free/en/papers/jin-2024-impact-reasoning-step-length-large/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Mingyu Jin, Qinkai Yu, Dong Shu, et al.*2024 · 119 citations*
12. [**Mechanistic Interpretability for AI Safety — A Review arXiv:2404.14082**Leonard Bereska, Efstratios Gavves*2024 · 108 citations*](https://md.research.elamur.ai/free/en/papers/bereska-2024-mechanistic-interpretability-ai-safety-review/)
13. **[Leveraging GPT-4 for Automatic Translation Post-Editing](https://md.research.elamur.ai/free/en/papers/raunak-2023-leveraging-gpt-4-automatic-translation/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Vikas Raunak, Amr Sharaf, Yiren Wang, et al.*2023 · 108 citations*
14. **[R-Judge: Benchmarking Safety Risk Awareness for LLM Agents](https://md.research.elamur.ai/free/en/papers/yuan-2024-r-judge-benchmarking-safety-risk/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Tongxin Yuan, Zhiwei He, Lingzhong Dong, et al.*2024 · 106 citations*
15. [**Multi-Agent Collaboration Mechanisms: A Survey of LLMs arXiv:2501.06322**Khanh-Tung Tran, Dung Dao, Minh-Duong Nguyen, et al.*2025 · 95 citations*](https://md.research.elamur.ai/free/en/papers/tran-2025-multi-agent-collaboration-mechanisms/)
16. **[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*
17. [**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/)
18. **[D4: Improving LLM Pretraining via Document De-Duplication and Diversification](https://md.research.elamur.ai/free/en/papers/tirumala-2023-d4-improving-llm-pretraining-document/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Kushal Tirumala, Daniel Simig, Armen Aghajanyan, et al.*2023 · 81 citations*
19. [**The Unreasonable Ineffectiveness of the Deeper Layers arXiv:2403.17887**Andrey Gromov, Kushal Tirumala, Hassan Shapourian, et al.*2024 · 71 citations*](https://md.research.elamur.ai/free/en/papers/gromov-2024-unreasonable-ineffectiveness-deeper-layers/)
20. [**Scaling Large Language Model-based Multi-Agent Collaboration arXiv:2406.07155**Chen Qian, Zihao Xie, YiFei Wang, et al.*2024 · 70 citations*](https://md.research.elamur.ai/free/en/papers/qian-2024-scaling-large-language-model/)
21. [**Goat: Fine-tuned LLaMA Outperforms GPT-4 on Arithmetic Tasks arXiv:2305.14201**Tiedong Liu, Bryan Kian Hsiang Low*2023 · 65 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-goat-fine-tuned-llama-outperforms/)
22. **[Can Language Models Handle Recursively Nested Grammatical Structures? A Case Study on Comparing Models and Humans](https://md.research.elamur.ai/free/en/papers/lampinen-2024-can-language-models-handle-recursively/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Andrew Lampinen*2024 · 63 citations*
23. **[One-Shot Learning as Instruction Data Prospector for Large Language Models](https://md.research.elamur.ai/free/en/papers/li-2024-one-shot-learning-as-instruction/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yunshui Li, Binyuan Hui, Xiaobo Xia, et al.*2024 · 61 citations*
24. **[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*
25. [**Language models scale reliably with over-training and on downstream tasks arXiv:2403.08540**Samir Yitzhak Gadre, Georgios Smyrnis, Vaishaal Shankar, et al.*2024 · 46 citations*](https://md.research.elamur.ai/free/en/papers/gadre-2024-language-models-scale-reliably-over/)
26. [**Big-Math: A Large-Scale, High-Quality Math Dataset for Reinforcement Learning in Language Models arXiv:2502.17387**Alon Albalak, Duy Phung, Nathan Lile, et al.*2025 · 43 citations*](https://md.research.elamur.ai/free/en/papers/albalak-2025-big-math-large-scale-high/)
27. [**Auxiliary task demands mask the capabilities of smaller language models arXiv:2404.02418**Jennifer Hu, Michael C. Frank*2024 · 41 citations*](https://md.research.elamur.ai/free/en/papers/hu-2024-auxiliary-task-demands-mask-capabilities/)
28. [**Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning Tasks arXiv:2402.04248**Jongho Park, Jaeseung Park, Zheyang Xiong, et al.*2024 · 41 citations*](https://md.research.elamur.ai/free/en/papers/park-2024-mamba-learn-learn-comparative-study/)
29. [**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/)
30. [**Evaluating Frontier Models for Dangerous Capabilities arXiv:2403.13793**Mary Phuong, Matthew Aitchison, Elliot Catt, et al.*2024 · 36 citations*](https://md.research.elamur.ai/free/en/papers/phuong-2024-evaluating-frontier-models-dangerous-capabilities/)
31. [**An Emulator for Fine-Tuning Large Language Models using Small Language Models arXiv:2310.12962**Eric Mitchell, Rafael Rafailov, Archit Sharma, et al.*2023 · 36 citations*](https://md.research.elamur.ai/free/en/papers/mitchell-2023-emulator-fine-tuning-large-language/)
32. [**Functional Benchmarks for Robust Evaluation of Reasoning Performance, and the Reasoning Gap arXiv:2402.19450**Saurabh Srivastava, Annarose M B, Anto P, et al.*2024 · 35 citations*](https://md.research.elamur.ai/free/en/papers/srivastava-2024-functional-benchmarks-robust-evaluation-reasoning/)
33. [**The Quantization Model of Neural Scaling arXiv:2303.13506**Eric J. Michaud, Ziming Liu, Uzay Girit, et al.*2023 · 30 citations*](https://md.research.elamur.ai/free/en/papers/michaud-2023-quantization-model-neural-scaling/)
34. **[S²R: Teaching LLMs to Self-verify and Self-correct via Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/ma-2025-s-2-r-teaching-llms/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ruotian Ma, Peisong Wang, Cheng Liu, et al.*2025 · 26 citations*
35. [**How predictable is language model benchmark performance? arXiv:2401.04757**David Owen*2024 · 19 citations*](https://md.research.elamur.ai/free/en/papers/owen-2024-predictable-language-model-benchmark-performance/)
36. **[Resolving Discrepancies in Compute-Optimal Scaling of Language Models](https://md.research.elamur.ai/free/en/papers/porian-2024-resolving-discrepancies-compute-optimal-scaling/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Tomer Porian, Mitchell Wortsman, Jenia Jitsev, et al.*2024 · 19 citations*
37. **[Zero-Shot Chain-of-Thought Reasoning Guided by Evolutionary Algorithms in Large Language Models](https://md.research.elamur.ai/free/en/papers/jin-2024-zero-shot-chain-thought-reasoning/) [Lecture notes in computer science](https://md.research.elamur.ai/free/en/venues/lecture-notes-in-computer-science/)**Feihu Jin, Yifan Liu, Ying Tan*2024 · 18 citations*
38. [**Kimina Lean Server: A High-Performance Lean Server for Large-Scale Verification arXiv:2504.21230**Marco Dos Santos, Hugues de Saxcé, Haiming Wang, et al.*2025 · 14 citations*](https://md.research.elamur.ai/free/en/papers/santos-2025-kimina-lean-server-high-performance/)
39. **[From Language to Cognition: How LLMs Outgrow the Human Language Network](https://md.research.elamur.ai/free/en/papers/alkhamissi-2025-language-cognition-how-llms-outgrow/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Badr AlKhamissi, Greta Tuckute, Yingtian Tang, et al.*2025 · 11 citations*
40. **[Improving Language Models Trained on Translated Data with Continual Pre-Training and Dictionary Learning Analysis](https://md.research.elamur.ai/free/en/papers/boughorbel-2024-improving-language-models-trained-translated/) [Arabic Natural Language Processing Conference (ArabicNLP)](https://md.research.elamur.ai/free/en/venues/arabicnlp/)**Sabri Boughorbel, Md Rizwan Parvez, Majd Hawasly*2024 · 8 citations*
41. **[Reviving DSP for Advanced Theorem Proving in the Era of Reasoning Models](https://md.research.elamur.ai/free/en/papers/cao-2025-reviving-dsp-advanced-theorem-proving/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Chenrui Cao, Liangcheng Song, Zenan Li, et al.*2025 · 7 citations*
42. [**Bourbaki: Self-Generated and Goal-Conditioned MDPs for Theorem Proving arXiv:2507.02726**Matthieu Zimmer, Xiaotong Ji, Rasul Tutunov, et al.*2025 · 5 citations*](https://md.research.elamur.ai/free/en/papers/zimmer-2025-bourbaki-self-generated-goal-conditioned/)
43. **[RLMEval: Evaluating Research-Level Neural Theorem Proving](https://md.research.elamur.ai/free/en/papers/poiroux-2025-rlmeval-evaluating-research-level-neural/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Auguste Poiroux, Antoine Bosselut, Viktor Kunčak*2025 · 5 citations*
44. **[Advancing Arabic Sentiment Analysis: ArSen Benchmark and the Improved Fuzzy Deep Hybrid Network](https://md.research.elamur.ai/free/en/papers/fang-2024-advancing-arabic-sentiment-analysis-arsen/) [Conference on Computational Natural Language Learning (CoNLL)](https://md.research.elamur.ai/free/en/venues/conll/)**Yang Fang, Cheng Xu, Shuhao Guan, et al.*2024 · 4 citations*
45. **[Scaling Laws for Code: Every Programming Language Matters](https://md.research.elamur.ai/free/en/papers/yang-2026-scaling-laws-code-every-programming/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jian Yang, Shuyue Guo, Linzheng Chai, et al.*2026 · 2 citations*
46. **[The Thin Line Between Comprehension and Persuasion in LLMs](https://md.research.elamur.ai/free/en/papers/wynter-2026-thin-line-between-comprehension-persuasion/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Adrian de Wynter, Tangming Yuan*2026 · 1 citation*
47. **[Bayesian Social Deduction with Graph-Informed Language Models](https://md.research.elamur.ai/free/en/papers/rahimirad-2026-bayesian-social-deduction-graph-informed/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shahab Rahimirad, Guven Gergerli, Lucia Romero, et al.*2026*
48. **[Toward Cognitive Alignment in Large Language Models: Integrating Linguistic Theory and Human Data](https://md.research.elamur.ai/free/en/papers/zaghouani-2026-cognitive-alignment-large-language-models/) [Workshop on Cognitive Modeling and Computational Linguistics (CMCL)](https://md.research.elamur.ai/free/en/venues/cmcl/)**Wajdi Zaghouani*2026*
49. **[Probing the Hidden Talent of ASR Foundation Models for L2 English Oral Assessment](https://md.research.elamur.ai/free/en/papers/chao-2025-probing-hidden-talent-asr-foundation/) [IEEE International Conference on Acoustics Speech and Signal Processing](https://md.research.elamur.ai/free/en/venues/ieee-international-conference-on-acoustics-speech-and-signal-processing/)**Fu-An Chao, Bi-Cheng Yan, Berlin Chen*2025*

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