---
title: "Who cites Zhun Liu"
description: "387 papers of the Elamur Research library cite the works of Zhun Liu, found in their bibliographies; the most cited first."
canonical: "https://research.elamur.ai/free/en/authors/zhun-liu/cited-by/"
---
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# Who cites Zhun Liu

387 citations of their works from 387 papers of our library (of the 140,884 whose bibliographies we read) · 46 with a page here, the most cited first · [← Zhun Liu](https://md.research.elamur.ai/free/en/authors/zhun-liu/)

1. [**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/)
2. [**Large language models are zero-shot reasoners**Takeshi Kojima, Shixiang Shane Gu, Machel Reid, et al.*2022 · 3,780 citations*](https://md.research.elamur.ai/free/en/papers/kojima-2022-large-language-models-zero-shot/)
3. [**A Survey on Evaluation of Large Language Models**Yupeng Chang, Xu Wang, Jindong Wang, et al.*2023 · 3,640 citations*](https://md.research.elamur.ai/free/en/papers/chang-2023-survey-evaluation-large-language-models/)
4. [**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/)
5. [**PaLM: Scaling Language Modeling with Pathways**Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, et al.*2022 · 2,603 citations*](https://md.research.elamur.ai/free/en/papers/chowdhery-2022-palm-scaling-language-modeling-pathways/)
6. [**OPT: Open Pre-trained Transformer Language Models**Susan Zhang, Stephen Roller, Naman Goyal, et al.*2022 · 1,709 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2022-opt-open-pre-trained-transformer/)
7. [**A Comprehensive Overview of Large Language Models**Humza Naveed, Asad Ullah Khan, Shi Qiu, et al.*2023 · 1,286 citations*](https://md.research.elamur.ai/free/en/papers/naveed-2023-comprehensive-overview-large-language-models/)
8. [**Training Compute-Optimal Large Language Models**Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, et al.*2022 · 1,110 citations*](https://md.research.elamur.ai/free/en/papers/hoffmann-2022-training-compute-optimal-large-language/)
9. [**MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models**Deyao Zhu, Jun Chen, Xiaoqian Shen, et al.*2023 · 1,105 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2023-minigpt-4-enhancing-vision-language/)
10. [**Solving Quantitative Reasoning Problems with Language Models**Aitor Lewkowycz, Anders Andreassen, David Dohan, et al.*2022 · 973 citations*](https://md.research.elamur.ai/free/en/papers/lewkowycz-2022-solving-quantitative-reasoning-problems-language/)
11. [**Is ChatGPT a General-Purpose Natural Language Processing Task Solver?**Chengwei Qin, Aston Zhang, Zhuosheng Zhang, et al.*2023 · 818 citations*](https://md.research.elamur.ai/free/en/papers/qin-2023-chatgpt-general-purpose-natural-language/)
12. [**GPT-NeoX-20B: An Open-Source Autoregressive Language Model**Sidney Black, Stella Biderman, Eric Hallahan, et al.*2022 · 818 citations*](https://md.research.elamur.ai/free/en/papers/black-2022-gpt-neox-20b-open-source/)
13. [**Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond**Jingfeng Yang, Hongye Jin, Ruixiang Tang, et al.*2023 · 806 citations*](https://md.research.elamur.ai/free/en/papers/yang-2023-harnessing-power-llms-practice-survey/)
14. [**GLM-130B: An Open Bilingual Pre-trained Model**Aohan Zeng, Xiao Liu, Zhengxiao Du, et al.*2022 · 775 citations*](https://md.research.elamur.ai/free/en/papers/zeng-2022-glm-130b-open-bilingual-pre/)
15. [**Crosslingual Generalization through Multitask Finetuning**Niklas Muennighoff, Thomas Wang, Lintang Sutawika, et al.*2022 · 692 citations*](https://md.research.elamur.ai/free/en/papers/muennighoff-2022-crosslingual-generalization-through-multitask-finetuning/)
16. [**Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes**Cheng-Yu Hsieh, Chun-Liang Li, Chih-kuan Yeh, et al.*2023 · 684 citations*](https://md.research.elamur.ai/free/en/papers/hsieh-2023-distilling-step-step-outperforming-larger/)
17. [**BloombergGPT: A Large Language Model for Finance**Shijie Wu, Ozan Irsoy, Steven Lu, et al.*2023 · 608 citations*](https://md.research.elamur.ai/free/en/papers/wu-2023-bloomberggpt-large-language-model-finance/)
18. [**GPT-NER: Named Entity Recognition via Large Language Models**Shuhe Wang, Xiaofei Sun, Xiaoya Li, et al.*2025 · 436 citations*](https://md.research.elamur.ai/free/en/papers/wang-2025-gpt-ner-named-entity-recognition/)
19. [**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/)
20. [**VILA: On Pre-training for Visual Language Models**Ji Lin, Hongxu Yin, Wei Ping, et al.*2023 · 367 citations*](https://md.research.elamur.ai/free/en/papers/lin-2023-vila-pre-training-visual-language/)
21. [**Language Is Not All You Need: Aligning Perception with Language Models**Shaohan Huang, Li Dong, Wenhui Wang, et al.*2023 · 319 citations*](https://md.research.elamur.ai/free/en/papers/huang-2023-language-all-need-aligning-perception/)
22. [**ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers**Zhewei Yao, Reza Yazdani Aminabadi, Minjia Zhang, et al.*2022 · 301 citations*](https://md.research.elamur.ai/free/en/papers/yao-2022-zeroquant-efficient-affordable-post-training/)
23. [**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/)
24. [**Reducing Activation Recomputation in Large Transformer Models**Vijay Korthikanti, Jared Casper, Sangkug Lym, et al.*2022 · 270 citations*](https://md.research.elamur.ai/free/en/papers/korthikanti-2022-reducing-activation-recomputation-large-transformer/)
25. [**Language Models can Solve Computer Tasks**Geunwoo Kim, Pierre Baldi, Stephen McAleer*2023 · 239 citations*](https://md.research.elamur.ai/free/en/papers/kim-2023-language-models-solve-computer-tasks/)
26. [**RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment**Hanze Dong, Wei Xiong, Deepanshu Goyal, et al.*2023 · 239 citations*](https://md.research.elamur.ai/free/en/papers/dong-2023-raft-reward-ranked-finetuning-generative/)
27. [**Teaching language models to support answers with verified quotes**Jacob Menick, Maja Trebacz, Vladimir Mikulik, et al.*2022 · 211 citations*](https://md.research.elamur.ai/free/en/papers/menick-2022-teaching-language-models-support-answers/)
28. [**PlanBench: An Extensible Benchmark for Evaluating Large Language Models on Planning and Reasoning about Change**Karthik Valmeekam, Matthew Marquez, Alberto Olmo, et al.*2022 · 209 citations*](https://md.research.elamur.ai/free/en/papers/valmeekam-2022-planbench-extensible-benchmark-evaluating-large/)
29. [**Improving Passage Retrieval with Zero-Shot Question Generation**Devendra Sachan, Mike Lewis, Mandar Joshi, et al.*2022 · 197 citations*](https://md.research.elamur.ai/free/en/papers/sachan-2022-improving-passage-retrieval-zero-shot/)
30. [**MiniGPT-v2: large language model as a unified interface for vision-language multi-task learning**Jun Chen, Deyao Zhu, Xiaoqian Shen, et al.*2023 · 192 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-minigpt-v2-large-language-model/)
31. [**Memorization Without Overfitting: Analyzing the Training Dynamics of Large Language Models**Kushal Tirumala, Aram H. Markosyan, Luke Zettlemoyer, et al.*2022 · 183 citations*](https://md.research.elamur.ai/free/en/papers/tirumala-2022-memorization-overfitting-analyzing-training-dynamics/)
32. [**Large Language Models are few(1)-shot Table Reasoners**Wenhu Chen*2023 · 182 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-large-language-models-are-few/)
33. [**SqueezeLLM: Dense-and-Sparse Quantization**Sehoon Kim, Coleman Hooper, Amir Gholami, et al.*2023 · 168 citations*](https://md.research.elamur.ai/free/en/papers/kim-2023-squeezellm-dense-sparse-quantization/)
34. [**DeepSpeed Ulysses: System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models**Sam Ade Jacobs, Masahiro Tanaka, Chengming Zhang, et al.*2023 · 159 citations*](https://md.research.elamur.ai/free/en/papers/jacobs-2023-deepspeed-ulysses-system-optimizations-enabling/)
35. [**Large Language Models with Controllable Working Memory**Daliang Li, Ankit Singh Rawat, Manzil Zaheer, et al.*2023 · 154 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-large-language-models-controllable-working/)
36. [**Leveraging Large Language Models for Multiple Choice Question Answering**Joshua Robinson, Christopher Michael Rytting, David Wingate*2022 · 154 citations*](https://md.research.elamur.ai/free/en/papers/robinson-2022-leveraging-large-language-models-multiple/)
37. [**The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset**Hugo Laurençon, Lucile Saulnier, Thomas Wang, et al.*2023 · 145 citations*](https://md.research.elamur.ai/free/en/papers/lauren-ccon-2023-bigscience-roots-corpus-1-6tb/)
38. [**A Stitch in Time Saves Nine: Detecting and Mitigating Hallucinations of LLMs by Validating Low-Confidence Generation**Neeraj Varshney; Wenlin Yao; Hongming Zhang; Jianshu Chen;, Dong Yu*2023 · 139 citations*](https://md.research.elamur.ai/free/en/papers/varshney-2023-stitch-time-saves-nine-detecting/)
39. [**ALLaM: Large Language Models for Arabic and English**M Saiful Bari, Yazeed Alnumay, Norah A. Alzahrani, et al.*2024 · 113 citations*](https://md.research.elamur.ai/free/en/papers/bari-2024-allam-large-language-models-arabic/)
40. [**D4: Improving LLM Pretraining via Document De-Duplication and Diversification**Kushal Tirumala, Daniel Simig, Armen Aghajanyan, et al.*2023 · 79 citations*](https://md.research.elamur.ai/free/en/papers/tirumala-2023-d4-improving-llm-pretraining-document/)
41. [**An Expanded Massive Multilingual Dataset for High-Performance Language Technologies (HPLT)**Laurie Burchell, Ona de Gibert, Nikolay Arefyev, et al.*2025 · 23 citations*](https://md.research.elamur.ai/free/en/papers/burchell-2025-expanded-massive-multilingual-dataset-high/)
42. [**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/)
43. [**Towards Ontology Construction with Language Models**Maurice Funk, Simon Hosemann, Jean Christoph Jung, et al.*2023 · 12 citations*](https://md.research.elamur.ai/free/en/papers/funk-2023-towards-ontology-construction-language-models/)
44. [**Adaptive Originality Filtering: Rejection Based Prompting and RiddleScore for Culturally Grounded Multilingual Riddle Generation**Duy Le, Kent Ziti, Evan Girard-Sun, et al.*2025 · 1 citation*](https://md.research.elamur.ai/free/en/papers/le-2025-adaptive-originality-filtering-rejection-based/)
45. [**Rationale-Guided Distillation for E-Commerce Relevance Classification: Bridging Large Language Models and Lightweight Cross-Encoders**Sanjay Agrawal, Faizan Ahemad, Vivek Varadarajan Sembium*2025 · 1 citation*](https://md.research.elamur.ai/free/en/papers/agrawal-2025-rationale-guided-distillation-e-commerce/)
46. [**Survey on Key Technologies for Large Language Model Pre-training Systems (大语言模型预训练系统关键技术综述)**Yanjie Gao (高彦杰), Yueguo Chen (陈跃国)*2025*](https://md.research.elamur.ai/free/en/papers/gao-2025-key-technologies-large/)

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

- [← back to](https://md.research.elamur.ai/free/en/authors/zhun-liu/) — Zhun Liu