---
title: "Who cites Joel Hestness"
description: "199 papers of the Elamur Research library cite the works of Joel Hestness, found in their bibliographies; the most cited first."
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---
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# Who cites Joel Hestness

200 citations of their works from 199 papers of our library (of the 140,884 whose bibliographies we read) · 42 with a page here, the most cited first · [← Joel Hestness](https://md.research.elamur.ai/free/en/authors/joel-hestness/)

1. [**Language Models are Few-Shot Learners**Tom B. Brown, Benjamin Mann, Nick Ryder, et al.*2020 · 10,782 citations*](https://md.research.elamur.ai/free/en/papers/brown-2020-language-models-few-shot-learners/)
2. [**Learning Transferable Visual Models From Natural Language Supervision**Alec Radford, Jong Wook Kim, Chris Hallacy, et al.*2021 · 8,295 citations*](https://md.research.elamur.ai/free/en/papers/radford-2021-learning-transferable-visual-models-natural/)
3. [**Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer**Colin Raffel, Noam Shazeer, Adam Roberts, et al.*2019 · 7,433 citations*](https://md.research.elamur.ai/free/en/papers/raffel-2019-exploring-limits-transfer-learning-unified/)
4. [**LLaMA: Open and Efficient Foundation Language Models**Hugo Touvron, Thibaut Lavril, Gautier Izacard, et al.*2023 · 5,609 citations*](https://md.research.elamur.ai/free/en/papers/touvron-2023-llama-open-efficient-foundation-language/)
5. [**On the Opportunities and Risks of Foundation Models**Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, et al.*2021 · 3,044 citations*](https://md.research.elamur.ai/free/en/papers/bommasani-2021-opportunities-risks-foundation-models/)
6. [**A Survey of Large Language Models**Wayne Xin Zhao, Kun Zhou, Junyi Li, et al.*2023 · 2,847 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2023-survey-large-language-models/)
7. [**Scaling Laws for Neural Language Models**Jared Kaplan, Sam McCandlish, Tom Henighan, et al.*2020 · 1,814 citations*](https://md.research.elamur.ai/free/en/papers/kaplan-2020-scaling-laws-neural-language-models/)
8. [**Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models**Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, et al.*2022 · 1,695 citations*](https://md.research.elamur.ai/free/en/papers/srivastava-2022-imitation-game-quantifying-extrapolating-capabilities/)
9. [**Least-to-most prompting enables complex reasoning in large language models***2022 · 1,143 citations*](https://md.research.elamur.ai/free/en/papers/al-2022-least-most-prompting-enables-complex/)
10. [**Reproducible scaling laws for contrastive language-image learning**Mehdi Cherti, Romain Beaumont, Ross Wightman, et al.*2022 · 750 citations*](https://md.research.elamur.ai/free/en/papers/cherti-2022-reproducible-scaling-laws-contrastive-language/)
11. [**Massively Multilingual Neural Machine Translation in the Wild: Findings and Challenges**Naveen Arivazhagan, Ankur Bapna, Orhan Firat, et al.*2019 · 555 citations*](https://md.research.elamur.ai/free/en/papers/arivazhagan-2019-massively-multilingual-neural-machine-translation/)
12. [**GLaM: Efficient Scaling of Language Models with Mixture-of-Experts**Nan Du, Yanping Huang, Andrew M. Dai, et al.*2021 · 474 citations*](https://md.research.elamur.ai/free/en/papers/du-2021-glam-efficient-scaling-language-models/)
13. [**The Falcon Series of Open Language Models**Ebtesam Almazrouei, Hamza Alobeidli, Abdulaziz Alshamsi, et al.*2023 · 436 citations*](https://md.research.elamur.ai/free/en/papers/almazrouei-2023-falcon-series-open-language-models/)
14. [**Are Emergent Abilities of Large Language Models a Mirage?**Rylan Schaeffer, Brando Miranda, Sanmi Koyejo*2023 · 323 citations*](https://md.research.elamur.ai/free/en/papers/schaeffer-2023-emergent-abilities-large-language-models/)
15. [**Challenges and Applications of Large Language Models**Jean Kaddour, Joshua Harris, Maximilian Mozes, et al.*2023 · 300 citations*](https://md.research.elamur.ai/free/en/papers/kaddour-2023-challenges-applications-large-language-models/)
16. [**RedPajama: an Open Dataset for Training Large Language Models**Maurice Weber, Daniel Fu, Quentin Anthony, et al.*2024 · 247 citations*](https://md.research.elamur.ai/free/en/papers/weber-2024-redpajama-open-dataset-training-large/)
17. [**Large Language Monkeys: Scaling Inference Compute with Repeated Sampling**Bradley Brown, Jordan Juravsky, Ryan Ehrlich, et al.*2024 · 241 citations*](https://md.research.elamur.ai/free/en/papers/brown-2024-large-language-monkeys-scaling-inference/)
18. [**Beyond neural scaling laws: beating power law scaling via data pruning**Ben Sorscher, Robert Geirhos, Shashank Shekhar, et al.*2022 · 220 citations*](https://md.research.elamur.ai/free/en/papers/sorscher-2022-neural-scaling-laws-beating-power/)
19. [**Fanar: An Arabic-Centric Multimodal Generative AI Platform**Fanar Team: Ummar Abbas, Mohammad Shahmeer Ahmad, Firoj Alam, et al.*2025 · 127 citations*](https://md.research.elamur.ai/free/en/papers/abbas-2025-fanar-arabic-centric-multimodal-generative/)
20. [**The case for 4-bit precision: k-bit Inference Scaling Laws**Tim Dettmers, Luke Zettlemoyer*2022 · 119 citations*](https://md.research.elamur.ai/free/en/papers/dettmers-2022-case-4-bit-precision-k/)
21. [**Scaling Laws for Autoregressive Generative Modeling**Tom Henighan, Jared Kaplan, Mor Katz, et al.*2020 · 115 citations*](https://md.research.elamur.ai/free/en/papers/henighan-2020-scaling-laws-autoregressive-generative-modeling/)
22. [**Inference Scaling Laws: An Empirical Analysis of Compute-Optimal Inference for Problem-Solving with Language Models**Yangzhen Wu, Zhiqing Sun, Shanda Li, et al.*2024 · 114 citations*](https://md.research.elamur.ai/free/en/papers/wu-2024-inference-scaling-laws-empirical-analysis/)
23. [**Scaling Laws for Transfer**Danny Hernandez, Jared Kaplan, Tom Henighan, et al.*2021 · 102 citations*](https://md.research.elamur.ai/free/en/papers/hernandez-2021-scaling-laws-transfer/)
24. [**SemDeDup: Data-efficient learning at web-scale through semantic deduplication**Amro Abbas, Kushal Tirumala, Dániel Simig, et al.*2023 · 94 citations*](https://md.research.elamur.ai/free/en/papers/abbas-2023-semdedup-data-efficient-learning-web/)
25. [**Scaling Laws and Interpretability of Learning from Repeated Data**Danny Hernandez, Tom Brown, Tom Conerly, et al.*2022 · 62 citations*](https://md.research.elamur.ai/free/en/papers/hernandez-2022-scaling-laws-interpretability-learning-repeated/)
26. [**Beyond Chinchilla-Optimal: Accounting for Inference in Language Model Scaling Laws**Nikhil Sardana, Jacob Portes, Sasha Doubov, et al.*2024 · 47 citations*](https://md.research.elamur.ai/free/en/papers/sardana-2024-chinchilla-optimal-accounting-inference-language/)
27. [**Scaling Laws for Generative Mixed-Modal Language Models**Armen Aghajanyan, Lili Yu, Alexis Conneau, et al.*2023 · 39 citations*](https://md.research.elamur.ai/free/en/papers/aghajanyan-2023-scaling-laws-generative-mixed-modal/)
28. [**Observational Scaling Laws and the Predictability of Language Model Performance**Yangjun Ruan, Chris J. Maddison, Tatsunori Hashimoto*2024 · 28 citations*](https://md.research.elamur.ai/free/en/papers/ruan-2024-observational-scaling-laws-predictability-language/)
29. [**The Quantization Model of Neural Scaling**Eric J. Michaud, Ziming Liu, Uzay Girit, et al.*2023 · 27 citations*](https://md.research.elamur.ai/free/en/papers/michaud-2023-quantization-model-neural-scaling/)
30. [**Distillation Scaling Laws**Dan Busbridge, Amitis Shidani, Floris Weers, et al.*2025 · 23 citations*](https://md.research.elamur.ai/free/en/papers/busbridge-2025-distillation-scaling-laws/)
31. [**ArMATH: a Dataset for Solving Arabic Math Word Problems**Reem Alghamdi, Zhenwen Liang, Xiangliang Zhang*2022 · 17 citations*](https://md.research.elamur.ai/free/en/papers/alghamdi-2022-armath-dataset-solving-arabic-math/)
32. [**Jais 2: A Family of Arabic-Centric Open Large Language Models**Mohamed Anwar, Abed Alhakim Freihat, George Ibrahim, et al.*2026 · 14 citations*](https://md.research.elamur.ai/free/en/papers/anwar-2026-jais-2-family-arabic-centric/)
33. [**Resolving Discrepancies in Compute-Optimal Scaling of Language Models**Tomer Porian, Mitchell Wortsman, Jenia Jitsev, et al.*2024 · 14 citations*](https://md.research.elamur.ai/free/en/papers/porian-2024-resolving-discrepancies-compute-optimal-scaling/)
34. [**Arabic Sentence Segmentation Across Genres and Punctuation Conditions**Mohammed Elkholy, Khalid N. Elmadani, Nizar Habash, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/elkholy-2026-arabic-sentence-segmentation-across-genres/)
35. [**Can Dialects Be Steered Like Languages? Sparse Neurons and Distributed Directions in Arabic LLMs**Kareem Elozeiri, Mervat Abassy, Omar Kallas, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/elozeiri-2026-dialects-steered-like-languages-sparse/)
36. [**Are Arabic Benchmarks Reliable? QIMMA's Quality-First Approach to LLM Evaluation**Leen AlQadi, Ahmed Alzubaidi, Mohammed Alyafeai, et al.*2026*](https://md.research.elamur.ai/free/en/papers/alqadi-2026-arabic-benchmarks-reliable-qimma-s/)
37. [**Beyond Cultural Knowledge: Evaluating Arabic Cultural Appropriateness of Large Language Models**Enes Altinisik, Hamdy Mubarak, Masoomali Fatehkia, et al.*2026*](https://md.research.elamur.ai/free/en/papers/altinisik-2026-cultural-knowledge-evaluating-arabic-cultural/)
38. [**Grounding Arabic LLMs in the Doha Historical Dictionary: Retrieval-Augmented Understanding of Quran and Hadith**Somaya Eltanbouly, Samer Rashwani*2026*](https://md.research.elamur.ai/free/en/papers/eltanbouly-2026-grounding-arabic-llms-doha-historical/)
39. [**SalamahBench: Dialect and Category Level Safety Evaluation of Arabic Language Models**Omar Abdelnasser, Fatemah Alharbi, Khaled Khasawneh, et al.*2026*](https://md.research.elamur.ai/free/en/papers/abdelnasser-2026-salamahbench-dialect-category-level-safety/)
40. [**Some Dialects Are More Equal Than Others: Non-Prestigious Arabic Dialectal Bias in LLMs**Mai Mohamed Eida, Ryan Dolan, Paul de Nijs, et al.*2026*](https://md.research.elamur.ai/free/en/papers/eida-2026-some-dialects-more-equal-than/)
41. [**When Similar Means Different: Evaluating LLMs on Arabic–Hebrew Cognates**Junhong Liang, Noor Abo Mokh, Bashar Alhafni*2026*](https://md.research.elamur.ai/free/en/papers/liang-2026-similar-means-different-evaluating-llms/)
42. [**YallaMorph: A Benchmark for Evaluating Arabic Morphological Generation in Large Language Models**Mahmoud Reda, Salam Khalifa, Reham Marzouk, et al.*2026*](https://md.research.elamur.ai/free/en/papers/reda-2026-yallamorph-benchmark-evaluating-arabic-morphological/)

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

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