---
title: "Who cites Carissa Schoenick — 2,111 papers of our library"
description: "2,111 papers of the Elamur Research library cite the works of Carissa Schoenick, found in their bibliographies · page 1 of 6 · 1–100; the most…"
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---
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# Who cites Carissa Schoenick

2,131 citations of their works from 2,111 papers of our library (of the 149,439 whose bibliographies we read) · 532 with a page here, the most cited first · page 1 of 6 · 1–100 · [← Carissa Schoenick](https://md.research.elamur.ai/free/en/authors/carissa-schoenick/)

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1. [**Language Models are Few-Shot Learners arXiv:2005.14165**Tom B. Brown, Benjamin Mann, Nick Ryder, et al.*2020 · 14,178 citations*](https://md.research.elamur.ai/free/en/papers/brown-2020-language-models-few-shot-learners/)
2. [**Llama 2: Open Foundation and Fine-Tuned Chat Models arXiv:2307.09288**Hugo Touvron, Louis Martin, Kevin Stone, et al.*2023 · 9,669 citations*](https://md.research.elamur.ai/free/en/papers/touvron-2023-llama-2-open-foundation-fine/)
3. [**LLaMA: Open and Efficient Foundation Language Models arXiv:2302.13971**Hugo Touvron, Thibaut Lavril, Gautier Izacard, et al.*2023 · 9,603 citations*](https://md.research.elamur.ai/free/en/papers/touvron-2023-llama-open-efficient-foundation-language/)
4. [**The Llama 3 Herd of Models arXiv:2407.21783**Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, et al.*2024 · 7,682 citations*](https://md.research.elamur.ai/free/en/papers/grattafiori-2024-llama-3-herd-models/)
5. [**PaLM: Scaling Language Modeling with Pathways arXiv:2204.02311**Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, et al.*2022 · 4,753 citations*](https://md.research.elamur.ai/free/en/papers/chowdhery-2022-palm-scaling-language-modeling-pathways/)
6. **[Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://md.research.elamur.ai/free/en/papers/zheng-2023-judging-llm-judge-mt-bench/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, et al.*2023 · 4,573 citations*
7. [**Measuring Massive Multitask Language Understanding arXiv:2009.03300**Dan Hendrycks, Collin Burns, Steven Basart, et al.*2020 · 3,620 citations*](https://md.research.elamur.ai/free/en/papers/hendrycks-2020-measuring-massive-multitask-language-understanding/)
8. [**On the Opportunities and Risks of Foundation Models arXiv:2108.07258**Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, et al.*2021 · 3,160 citations*](https://md.research.elamur.ai/free/en/papers/bommasani-2021-opportunities-risks-foundation-models/)
9. [**Self-Consistency Improves Chain of Thought Reasoning in Language Models arXiv:2203.11171**Xuezhi Wang, Jason Wei, Dale Schuurmans, et al.*2022 · 3,121 citations*](https://md.research.elamur.ai/free/en/papers/wang-2022-self-consistency-improves-chain-thought/)
10. **[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*
11. [**Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge arXiv:1803.05457**Peter Clark, Isaac Cowhey, Oren Etzioni, et al.*2018 · 2,394 citations*](https://md.research.elamur.ai/free/en/papers/clark-2018-think-have-solved-question-answering/)
12. [**GPT-4o System Card arXiv:2410.21276**OpenAI, Aaron Hurst, Adam Lerer, et al.*2024 · 2,304 citations*](https://md.research.elamur.ai/free/en/papers/openai-2024-gpt-4o-system-card/)
13. **[TruthfulQA: Measuring How Models Mimic Human Falsehoods](https://md.research.elamur.ai/free/en/papers/lin-2022-truthfulqa-measuring-how-models-mimic/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Stephanie Lin, Jacob Hilton, Owain Evans*2022 · 2,088 citations*
14. [**Finetuned Language Models Are Zero-Shot Learners arXiv:2109.01652**Jason Wei, Maarten Bosma, Vincent Y. Zhao, et al.*2021 · 1,954 citations*](https://md.research.elamur.ai/free/en/papers/wei-2021-finetuned-language-models-zero-shot/)
15. [**Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models arXiv:2206.04615 · Transactions on Machine Learning Research, May/2022, https://openreview.net/forum?id=uyTL5Bvosj**Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, et al.*2022 · 1,756 citations*](https://md.research.elamur.ai/free/en/papers/srivastava-2022-imitation-game-quantifying-extrapolating-capabilities/)
16. [**OPT: Open Pre-trained Transformer Language Models arXiv:2205.01068**Susan Zhang, Stephen Roller, Naman Goyal, et al.*2022 · 1,755 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2022-opt-open-pre-trained-transformer/)
17. [**Qwen Technical Report arXiv:2309.16609**Jinze Bai, Shuai Bai, Yunfei Chu, et al.*2023 · 1,669 citations*](https://md.research.elamur.ai/free/en/papers/bai-2023-qwen-technical-report/)
18. [**Retrieval-Augmented Generation for Large Language Models: A Survey arXiv:2312.10997**Yunfan Gao, Yun Xiong, Xinyu Gao, et al.*2023 · 1,663 citations*](https://md.research.elamur.ai/free/en/papers/gao-2023-retrieval-augmented-generation-large-language/)
19. [**Multitask Prompted Training Enables Zero-Shot Task Generalization arXiv:2110.08207**Victor Sanh, Albert Webson, Colin Raffel, et al.*2021 · 1,612 citations*](https://md.research.elamur.ai/free/en/papers/sanh-2021-multitask-prompted-training-enables-zero/)
20. **[Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?](https://md.research.elamur.ai/free/en/papers/min-2022-rethinking-role-demonstrations-what-makes/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Sewon Min, Xinxi Lyu, Ari Holtzman, et al.*2022 · 1,444 citations*
21. **[A Comprehensive Overview of Large Language Models](https://md.research.elamur.ai/free/en/papers/naveed-2023-comprehensive-overview-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/)**Humza Naveed, Asad Ullah Khan, Shi Qiu, et al.*2023 · 1,287 citations*
22. **[PIQA: Reasoning about Physical Commonsense in Natural Language](https://md.research.elamur.ai/free/en/papers/bisk-2019-piqa-reasoning-about-physical-commonsense/) [Proceedings of the AAAI Conference on Artificial Intelligence](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-aaai-conference-on-artificial-intelligence/)**Yonatan Bisk, Rowan Zellers, Ronan Le Bras, et al.*2019 · 1,264 citations*
23. [**MS MARCO: A Human Generated MAchine Reading COmprehension Dataset arXiv:1611.09268**Payal Bajaj, Daniel Campos, Nick Craswell, et al.*2016 · 1,138 citations*](https://md.research.elamur.ai/free/en/papers/bajaj-2016-ms-marco-human-generated/)
24. [**Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone arXiv:2404.14219**Marah Abdin, Jyoti Aneja, Hany Awadalla, et al.*2024 · 1,134 citations*](https://md.research.elamur.ai/free/en/papers/abdin-2024-phi-3-technical-report-highly/)
25. [**Qwen2.5-VL Technical Report arXiv:2502.13923**Shuai Bai, Keqin Chen, Xuejing Liu, et al.*2025 · 1,031 citations*](https://md.research.elamur.ai/free/en/papers/bai-2025-qwen2-5-vl-technical/)
26. [**PaLM 2 Technical Report arXiv:2305.10403**Rohan Anil, Andrew M. Dai, Orhan Firat, et al.*2023 · 1,000 citations*](https://md.research.elamur.ai/free/en/papers/anil-2023-palm-2-technical-report/)
27. [**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/)
28. **[UNIFIEDQA: Crossing Format Boundaries with a Single QA System](https://md.research.elamur.ai/free/en/papers/khashabi-2020-unifiedqa-crossing-format-boundaries-single/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Daniel Khashabi, Sewon Min, Tushar Khot, et al.*2020 · 857 citations*
29. **[GPT-NeoX-20B: An Open-Source Autoregressive Language Model](https://md.research.elamur.ai/free/en/papers/black-2022-gpt-neox-20b-open-source/) [BigScience Episode – Workshop on Challenges & Perspectives in Creating Large Language Models](https://md.research.elamur.ai/free/en/venues/bigscience/)**Sidney Black, Stella Biderman, Eric Hallahan, et al.*2022 · 831 citations*
30. **[Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond](https://md.research.elamur.ai/free/en/papers/yang-2023-harnessing-power-llms-practice-survey/) [ACM Transactions on Knowledge Discovery from Data](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-knowledge-discovery-from-data/)**Jingfeng Yang, Hongye Jin, Ruixiang Tang, et al.*2023 · 814 citations*
31. [**GLM-130B: An Open Bilingual Pre-trained Model arXiv:2210.02414**Aohan Zeng, Xiao Liu, Zhengxiao Du, et al.*2022 · 786 citations*](https://md.research.elamur.ai/free/en/papers/zeng-2022-glm-130b-open-bilingual-pre/)
32. **[Towards Reasoning in Large Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/huang-2023-reasoning-large-language-models-survey/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jie Huang, Kevin Chen-Chuan Chang*2023 · 780 citations*
33. **[MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark](https://md.research.elamur.ai/free/en/papers/wang-2024-mmlu-pro-more-robust-challenging/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Yubo Wang, Xueguang Ma, Ge Zhang, et al.*2024 · 761 citations*
34. **[WizardLM: Empowering large pre-trained language models to follow complex instructions](https://md.research.elamur.ai/free/en/papers/xu-2023-wizardlm-empowering-large-pre-trained/) [International Conference on Learning Representations (ICLR)](https://md.research.elamur.ai/free/en/venues/international-conference-on-learning-representations-iclr/)**Can Xu, Qingfeng Sun, Kai Zheng, et al.*2023 · 741 citations*
35. **[KILT: a Benchmark for Knowledge Intensive Language Tasks](https://md.research.elamur.ai/free/en/papers/petroni-2020-kilt-benchmark-knowledge-intensive-language/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Fabio Petroni, Aleksandra Piktus, Angela Fan, et al.*2020 · 678 citations*
36. [**Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection arXiv:2310.11511**Akari Asai, Zeqiu Wu, Yizhong Wang, et al.*2023 · 677 citations*](https://md.research.elamur.ai/free/en/papers/asai-2023-self-rag-learning-retrieve-generate/)
37. [**Atlas: Few-shot Learning with Retrieval Augmented Language Models arXiv:2208.03299**Gautier Izacard, Patrick Lewis, Maria Lomeli, et al.*2022 · 673 citations*](https://md.research.elamur.ai/free/en/papers/izacard-2022-atlas-few-shot-learning-retrieval/)
38. [**BloombergGPT: A Large Language Model for Finance arXiv:2303.17564**Shijie Wu, Ozan Irsoy, Steven Lu, et al.*2023 · 618 citations*](https://md.research.elamur.ai/free/en/papers/wu-2023-bloomberggpt-large-language-model-finance/)
39. [**The Flan Collection: Designing Data and Methods for Effective Instruction Tuning arXiv:2301.13688**Shayne Longpre, Le Hou, Tu Vu, et al.*2023 · 607 citations*](https://md.research.elamur.ai/free/en/papers/longpre-2023-flan-collection-designing-data-methods/)
40. **[Large Language Models Can Self-Improve](https://md.research.elamur.ai/free/en/papers/huang-2023-large-language-models-can-self/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jiaxin Huang, Shixiang Gu, Le Hou, et al.*2023 · 575 citations*
41. [**What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams arXiv:2009.13081**Di Jin, Eileen Pan, Nassim Oufattole, et al.*2020 · 567 citations*](https://md.research.elamur.ai/free/en/papers/jin-2020-disease-this-patient-have-large/)
42. [**Qwen2.5-Coder Technical Report arXiv:2409.12186**Binyuan Hui, Jian Yang, Zeyu Cui, et al.*2024 · 559 citations*](https://md.research.elamur.ai/free/en/papers/hui-2024-qwen2-5-coder-technical-report/)
43. [**Unleashing the potential of prompt engineering for large language models arXiv:2310.14735 · Patterns 6(6) 101,260 (2025)**Banghao Chen, Zhaofeng Zhang, Nicolas Langrene, et al.*2023 · 536 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-unleashing-potential-prompt-engineering-large/)
44. **[Crowdsourcing Multiple Choice Science Questions](https://md.research.elamur.ai/free/en/papers/welbl-2017-crowdsourcing-multiple-choice-science-questions/) [Workshop on Noisy User-generated Text](https://md.research.elamur.ai/free/en/venues/wnut/)**Johannes Welbl, Nelson F. Liu, Matt Gardner*2017 · 524 citations*
45. **[Instruction Tuning for Large Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/zhang-2023-instruction-tuning-large-language-models/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Shengyu Zhang, Linfeng Dong, Xiaoya Li, et al.*2023 · 520 citations*
46. [**Few-shot Learning with Multilingual Language Models arXiv:2112.10668**Xi Victoria Lin, Todor Mihaylov, Mikel Artetxe, et al.*2021 · 500 citations*](https://md.research.elamur.ai/free/en/papers/lin-2021-few-shot-learning-multilingual-language/)
47. **[BBQ: A hand-built bias benchmark for question answering](https://md.research.elamur.ai/free/en/papers/parrish-2022-bbq-hand-built-bias-benchmark/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Alicia Parrish, Angelica Chen, Nikita Nangia, et al.*2022 · 497 citations*
48. [**GLaM: Efficient Scaling of Language Models with Mixture-of-Experts arXiv:2112.06905**Nan Du, Yanping Huang, Andrew M. Dai, et al.*2021 · 496 citations*](https://md.research.elamur.ai/free/en/papers/du-2021-glam-efficient-scaling-language-models/)
49. [**Large Language Models: A Survey arXiv:2402.06196**Shervin Minaee, Tomas Mikolov, Narjes Nikzad, et al.*2024 · 471 citations*](https://md.research.elamur.ai/free/en/papers/minaee-2024-large-language-models-survey/)
50. **[LLM-Pruner: On the Structural Pruning of Large Language Models](https://md.research.elamur.ai/free/en/papers/ma-2023-llm-pruner-structural-pruning-large/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Xinyin Ma, Gongfan Fang, Xinchao Wang*2023 · 452 citations*
51. [**FreeLB: Enhanced Adversarial Training for Natural Language Understanding arXiv:1909.11764**Chen Zhu, Yu Cheng, Zhe Gan, et al.*2019 · 452 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2019-freelb-enhanced-adversarial-training-natural/)
52. [**The Falcon Series of Open Language Models arXiv:2311.16867**Ebtesam Almazrouei, Hamza Alobeidli, Abdulaziz Alshamsi, et al.*2023 · 443 citations*](https://md.research.elamur.ai/free/en/papers/almazrouei-2023-falcon-series-open-language-models/)
53. **[The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale](https://md.research.elamur.ai/free/en/papers/penedo-2024-fineweb-datasets-decanting-web-finest/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Guilherme Penedo, Hynek Kydlíček, Loubna Ben allal, et al.*2024 · 434 citations*
54. [**Deep Learning: A Critical Appraisal arXiv:1801.00631**Gary Marcus*2018 · 417 citations*](https://md.research.elamur.ai/free/en/papers/marcus-2018-deep-learning-critical-appraisal/)
55. [**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/)
56. **[LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models](https://md.research.elamur.ai/free/en/papers/hu-2023-llm-adapters-adapter-family-parameter/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Zhiqiang Hu, Lei Wang, Yihuai Lan, et al.*2023 · 401 citations*
57. [**Representation Engineering: A Top-Down Approach to AI Transparency arXiv:2310.01405**Andy Zou, Long Phan, Sarah Chen, et al.*2023 · 397 citations*](https://md.research.elamur.ai/free/en/papers/zou-2023-representation-engineering-top-down-approach/)
58. [**CMMLU: Measuring massive multitask language understanding in Chinese arXiv:2306.09212**Haonan Li, Yixuan Zhang, Fajri Koto, et al.*2023 · 387 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-cmmlu-measuring-massive-multitask-language/)
59. [**Qwen3-VL Technical Report arXiv:2511.21631**Shuai Bai, Yuxuan Cai, Ruizhe Chen, et al.*2025 · 370 citations*](https://md.research.elamur.ai/free/en/papers/bai-2025-qwen3-vl-technical-report/)
60. [**TinyLlama: An Open-Source Small Language Model arXiv:2401.02385**Peiyuan Zhang, Guangtao Zeng, Tianduo Wang, et al.*2024 · 352 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2024-tinyllama-open-source-small-language/)
61. [**Kimi K2: Open Agentic Intelligence arXiv:2507.20534**Kimi Team, Yifan Bai, Yiping Bao, et al.*2025 · 347 citations*](https://md.research.elamur.ai/free/en/papers/kimi-team-2025-kimi-k2-open-agentic/)
62. [**UL2: Unifying Language Learning Paradigms arXiv:2205.05131**Yi Tay, Mostafa Dehghani, Vinh Q. Tran, et al.*2022 · 347 citations*](https://md.research.elamur.ai/free/en/papers/tay-2022-ul2-unifying-language-learning-paradigms/)
63. [**LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention arXiv:2303.16199**Renrui Zhang, Jiaming Han, Chris Liu, et al.*2023 · 333 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-llama-adapter-efficient-fine-tuning/)
64. [**The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence arXiv:2002.06177**Gary Marcus*2020 · 332 citations*](https://md.research.elamur.ai/free/en/papers/marcus-2020-next-decade-ai-four/)
65. [**Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning arXiv:2503.09516**Bowen Jin, Hansi Zeng, Zhenrui Yue, et al.*2025 · 322 citations*](https://md.research.elamur.ai/free/en/papers/jin-2025-search-r1-training-llms-reason/)
66. **[Muppet: Massive Multi-task Representations with Pre-Finetuning](https://md.research.elamur.ai/free/en/papers/aghajanyan-2021-muppet-massive-multi-task-representations/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Armen Aghajanyan, Anchit Gupta, Akshat Shrivastava, et al.*2021 · 307 citations*
67. **[Scalable Multi-Hop Relational Reasoning for Knowledge-Aware Question Answering](https://md.research.elamur.ai/free/en/papers/feng-2020-scalable-multi-hop-relational-reasoning/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yanlin Feng, Xinyue Chen, Bill Yuchen Lin, et al.*2020 · 300 citations*
68. **[xLSTM: Extended Long Short-Term Memory](https://md.research.elamur.ai/free/en/papers/beck-2024-xlstm-extended-long-short-term/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Maximilian Beck, Korbinian Pöppel, Markus Spanring, et al.*2024 · 296 citations*
69. **[Baize: An Open-Source Chat Model with Parameter-Efficient Tuning on Self-Chat Data](https://md.research.elamur.ai/free/en/papers/xu-2023-baize-open-source-chat-model/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Canwen Xu, Daya Guo, Nan Duan, et al.*2023 · 296 citations*
70. [**Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling arXiv:2412.05271**Zhe Chen, Weiyun Wang, Yue Cao, et al.*2024 · 287 citations*](https://md.research.elamur.ai/free/en/papers/chen-2024-expanding-performance-boundaries-open/)
71. [**ReClor: A Reading Comprehension Dataset Requiring Logical Reasoning arXiv:2002.04326**Weihao Yu, Zihang Jiang, Yanfei Dong, et al.*2020 · 286 citations*](https://md.research.elamur.ai/free/en/papers/yu-2020-reclor-reading-comprehension-dataset-requiring/)
72. **[Surface Form Competition: Why the Highest Probability Answer Isn't Always Right](https://md.research.elamur.ai/free/en/papers/holtzman-2021-surface-form-competition-why-highest/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Ari Holtzman, Peter West, Vered Shwartz, et al.*2021 · 277 citations*
73. **[LLM-QAT: Data-Free Quantization Aware Training for Large Language Models](https://md.research.elamur.ai/free/en/papers/liu-2024-llm-qat-data-free-quantization/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zechun Liu, Barlas Oguz, Changsheng Zhao, et al.*2024 · 276 citations*
74. **[PowerInfer: Fast Large Language Model Serving with a Consumer-grade GPU](https://md.research.elamur.ai/free/en/papers/song-2023-powerinfer-fast-large-language-model/) [ACM SIGOPS Symposium on Operating Systems Principles (SOSP)](https://md.research.elamur.ai/free/en/venues/acm-sigops-symposium-on-operating-systems-principles-sosp/)**Yixin Song, Zeyu Mi, Haotong Xie, et al.*2023 · 272 citations*
75. [**MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies arXiv:2404.06395**Shengding Hu, Yuge Tu, Xu Han, et al.*2024 · 269 citations*](https://md.research.elamur.ai/free/en/papers/hu-2024-minicpm-unveiling-potential-small-language/)
76. **[The Effect of Sampling Temperature on Problem Solving in Large Language Models](https://md.research.elamur.ai/free/en/papers/renze-2024-effect-sampling-temperature-problem-solving/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Matthew Renze*2024 · 263 citations*
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