---
title: "Who cites Jekaterina Novikova"
description: "1,757 papers of the Elamur Research library cite the works of Jekaterina Novikova, found in their bibliographies · page 1 of 2 · 1–100; the most cited…"
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---
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# Who cites Jekaterina Novikova

1,819 citations of their works from 1,757 papers of our library (of the 140,884 whose bibliographies we read) · 180 with a page here, the most cited first · page 1 of 2 · 1–100 · [← Jekaterina Novikova](https://md.research.elamur.ai/free/en/authors/jekaterina-novikova/)

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1. [**LoRA: Low-Rank Adaptation of Large Language Models**Edward J. Hu, Yelong Shen, Phillip Wallis, et al.*2021 · 5,972 citations*](https://md.research.elamur.ai/free/en/papers/hu-2021-lora-low-rank-adaptation-large/)
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. [**HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering**Zhilin Yang, Peng Qi, Saizheng Zhang, et al.*2018 · 3,489 citations*](https://md.research.elamur.ai/free/en/papers/yang-2018-hotpotqa-dataset-diverse-explainable-multi/)
5. [**Qwen3 Technical Report**An Yang, Anfeng Li, Baosong Yang, et al.*2025 · 3,276 citations*](https://md.research.elamur.ai/free/en/papers/yang-2025-qwen3-technical-report/)
6. [**Gemini: A Family of Highly Capable Multimodal Models**Gemini Team, Rohan Anil, Sebastian Borgeaud, et al.*2023 · 2,951 citations*](https://md.research.elamur.ai/free/en/papers/gemini-team-2023-gemini-family-highly-capable-multimodal/)
7. [**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/)
8. [**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/)
9. [**Sparks of Artificial General Intelligence: Early experiments with GPT-4**Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, et al.*2023 · 2,389 citations*](https://md.research.elamur.ai/free/en/papers/bubeck-2023-sparks-artificial-general-intelligence-early/)
10. [**Emergent Abilities of Large Language Models**Jason Wei, Yi Tay, Rishi Bommasani, et al.*2022 · 2,215 citations*](https://md.research.elamur.ai/free/en/papers/wei-2022-emergent-abilities-large-language-models/)
11. [**Scaling Instruction-Finetuned Language Models**Hyung Won Chung, Le Hou, Shayne Longpre, et al.*2022 · 2,176 citations*](https://md.research.elamur.ai/free/en/papers/chung-2022-scaling-instruction-finetuned-language-models/)
12. [**Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context**Gemini Team, Petko Georgiev, Ving Ian Lei, et al.*2024 · 1,522 citations*](https://md.research.elamur.ai/free/en/papers/gemini-team-2024-gemini-1-5-unlocking-multimodal/)
13. [**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/)
14. [**Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone**Marah Abdin, Jyoti Aneja, Hany Awadalla, et al.*2024 · 1,113 citations*](https://md.research.elamur.ai/free/en/papers/abdin-2024-phi-3-technical-report-highly/)
15. [**Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them**Mirac Suzgun, Nathan Scales, Nathanael Schärli, et al.*2023 · 1,094 citations*](https://md.research.elamur.ai/free/en/papers/suzgun-2023-challenging-big-bench-tasks-whether/)
16. [**MTEB: Massive Text Embedding Benchmark**Niklas Muennighoff, Nouamane Tazi, Loïc Magne, et al.*2022 · 1,021 citations*](https://md.research.elamur.ai/free/en/papers/muennighoff-2022-mteb-massive-text-embedding-benchmark/)
17. [**PaLM 2 Technical Report**Rohan Anil, Andrew M. Dai, Orhan Firat, et al.*2023 · 971 citations*](https://md.research.elamur.ai/free/en/papers/anil-2023-palm-2-technical-report/)
18. [**GPQA: A Graduate-Level Google-Proof Q&A Benchmark**David Rein, Betty Li Hou, Asa Cooper Stickland, et al.*2023 · 964 citations*](https://md.research.elamur.ai/free/en/papers/rein-2023-gpqa-graduate-level-google-proof/)
19. [**Constitutional AI: Harmlessness from AI Feedback**Yuntao Bai, Saurav Kadavath, Sandipan Kundu, et al.*2022 · 957 citations*](https://md.research.elamur.ai/free/en/papers/bai-2022-constitutional-ai-harmlessness-ai-feedback/)
20. [**A Survey on In-context Learning**Qingxiu Dong, Lei Li, Damai Dai, et al.*2023 · 954 citations*](https://md.research.elamur.ai/free/en/papers/dong-2023-survey-context-learning/)
21. [**C-Pack: Packed Resources For General Chinese Embeddings**Shitao Xiao, Zheng Liu, Peitian Zhang, et al.*2023 · 920 citations*](https://md.research.elamur.ai/free/en/papers/xiao-2023-c-pack-packed-resources/)
22. [**A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity**Yejin Bang, Samuel Cahyawijaya, Nayeon Lee, et al.*2023 · 866 citations*](https://md.research.elamur.ai/free/en/papers/bang-2023-multitask-multilingual-multimodal-evaluation-chatgpt/)
23. [**ChatGPT: Jack of all trades, master of none**Jan Kocon, Igor Cichecki, Oliwier Kaszyca, et al.*2023 · 863 citations*](https://md.research.elamur.ai/free/en/papers/kocon-2023-chatgpt-jack-all-trades-master/)
24. [**MoverScore: Text Generation Evaluating with Contextualized Embeddings and Earth Mover Distance**Wei Zhao, Maxime Peyrard, Fei Liu, et al.*2019 · 822 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2019-moverscore-text-generation-evaluating-contextualized/)
25. [**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/)
26. [**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/)
27. [**Can Large Language Models Transform Computational Social Science?**Caleb Ziems, William Held, Omar Shaikh, et al.*2023 · 776 citations*](https://md.research.elamur.ai/free/en/papers/ziems-2023-large-language-models-transform-computational/)
28. [**Towards Reasoning in Large Language Models: A Survey**Jie Huang, Kevin Chen-Chuan Chang*2023 · 775 citations*](https://md.research.elamur.ai/free/en/papers/huang-2023-reasoning-large-language-models-survey/)
29. [**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/)
30. [**MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark**Yubo Wang, Xueguang Ma, Ge Zhang, et al.*2024 · 731 citations*](https://md.research.elamur.ai/free/en/papers/wang-2024-mmlu-pro-more-robust-challenging/)
31. [**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/)
32. [**ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools**Team GLM, Aohan Zeng, Bin Xu, et al.*2024 · 687 citations*](https://md.research.elamur.ai/free/en/papers/glm-2024-chatglm-family-large-language-models/)
33. [**Large Language Models Are Human-Level Prompt Engineers**Yongchao Zhou, Andrei Ioan Muresanu, Ziwen Han, et al.*2022 · 677 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2022-large-language-models-human-level/)
34. [**Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate**Tian Liang, Zhiwei He, Wenxiang Jiao, et al.*2023 · 643 citations*](https://md.research.elamur.ai/free/en/papers/liang-2023-encouraging-divergent-thinking-large-language/)
35. [**SWE-bench: Can Language Models Resolve Real-World GitHub Issues?**Carlos E. Jimenez, John Yang, Alexander Wettig, et al.*2024 · 617 citations*](https://md.research.elamur.ai/free/en/papers/jimenez-2024-swe-bench-can-language-models/)
36. [**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/)
37. [**Towards a Unified View of Parameter-Efficient Transfer Learning**Junxian He, Chunting Zhou, Xuezhe Ma, et al.*2021 · 604 citations*](https://md.research.elamur.ai/free/en/papers/he-2021-towards-unified-view-parameter-efficient/)
38. [**The Flan Collection: Designing Data and Methods for Effective Instruction Tuning**Shayne Longpre, Le Hou, Tu Vu, et al.*2023 · 590 citations*](https://md.research.elamur.ai/free/en/papers/longpre-2023-flan-collection-designing-data-methods/)
39. [**s1: Simple test-time scaling**Niklas Muennighoff, Zitong Yang, Weijia Shi, et al.*2025 · 584 citations*](https://md.research.elamur.ai/free/en/papers/muennighoff-2025-s1-simple-test-time-scaling/)
40. [**Towards a Human-like Open-Domain Chatbot**Daniel Adiwardana, Minh-Thang Luong, David R. So, et al.*2020 · 573 citations*](https://md.research.elamur.ai/free/en/papers/adiwardana-2020-towards-human-like-open-domain/)
41. [**Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting**Miles Turpin, Julian Michael, Ethan Perez, et al.*2023 · 568 citations*](https://md.research.elamur.ai/free/en/papers/turpin-2023-language-models-don-t-always/)
42. [**Improving Factuality and Reasoning in Language Models through Multiagent Debate**Yilun Du, Shuang Li, Antonio Torralba, et al.*2023 · 564 citations*](https://md.research.elamur.ai/free/en/papers/du-2023-improving-factuality-reasoning-language-models/)
43. [**Large Language Models Can Self-Improve**Jiaxin Huang, Shixiang Gu, Le Hou, et al.*2023 · 560 citations*](https://md.research.elamur.ai/free/en/papers/huang-2023-large-language-models-can-self/)
44. [**A Structured Review of the Validity of BLEU**Ehud Reiter*2018 · 538 citations*](https://md.research.elamur.ai/free/en/papers/reiter-2018-structured-review-validity-bleu/)
45. [**Instruction Tuning for Large Language Models: A Survey**Shengyu Zhang, Linfeng Dong, Xiaoya Li, et al.*2023 · 515 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-instruction-tuning-large-language-models/)
46. [**Baichuan 2: Open Large-scale Language Models**Aiyuan Yang, Bin Xiao, Bingning Wang, et al.*2023 · 512 citations*](https://md.research.elamur.ai/free/en/papers/yang-2023-baichuan-2-open-large-scale/)
47. [**LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding**Yushi Bai, Xin Lv, Jiajie Zhang, et al.*2023 · 505 citations*](https://md.research.elamur.ai/free/en/papers/bai-2023-longbench-bilingual-multitask-benchmark/)
48. [**ToTTo: A Controlled Table-To-Text Generation Dataset**Ankur Parikh, Xuezhi Wang, Sebastian Gehrmann, et al.*2020 · 479 citations*](https://md.research.elamur.ai/free/en/papers/parikh-2020-totto-controlled-table-text-generation/)
49. [**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/)
50. [**C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models**Yuzhen Huang, Yuzhuo Bai, Zhihao Zhu, et al.*2023 · 427 citations*](https://md.research.elamur.ai/free/en/papers/huang-2023-c-eval-multi-level-multi/)
51. [**Diffusion-LM Improves Controllable Text Generation**Xiang Lisa Li, John Thickstun, Ishaan Gulrajani, et al.*2022 · 421 citations*](https://md.research.elamur.ai/free/en/papers/li-2022-diffusion-lm-improves-controllable-text/)
52. [**Automatic Detection of Generated Text is Easiest when Humans are Fooled**Daphne Ippolito, Daniel Duckworth, Chris Callison-Burch, et al.*2019 · 406 citations*](https://md.research.elamur.ai/free/en/papers/ippolito-2019-automatic-detection-generated-text-easiest/)
53. [**UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models**Tianbao Xie, Chen Henry Wu, Peng Shi, et al.*2022 · 405 citations*](https://md.research.elamur.ai/free/en/papers/xie-2022-unifiedskg-unifying-multi-tasking-structured/)
54. [**AgentBench: Evaluating LLMs as Agents**Xiao Liu, Hao Yu, Hanchen Zhang, et al.*2023 · 404 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-agentbench-evaluating-llms-agents/)
55. [**Yi: Open Foundation Models by 01.AI**Alex Young, Bei Chen, Chao Li, et al.*2024 · 402 citations*](https://md.research.elamur.ai/free/en/papers/ai-2024-yi-open-foundation-models-01/)
56. [**Whose Opinions Do Language Models Reflect?**Shibani Santurkar, Esin Durmus, Faisal Ladhak, et al.*2023 · 390 citations*](https://md.research.elamur.ai/free/en/papers/santurkar-2023-whose-opinions-language-models-reflect/)
57. [**Large Language Models as Optimizers**Chengrun Yang, Xuezhi Wang, Yifeng Lu, et al.*2023 · 388 citations*](https://md.research.elamur.ai/free/en/papers/yang-2023-large-language-models-optimizers/)
58. [**CMMLU: Measuring massive multitask language understanding in Chinese**Haonan Li, Yixuan Zhang, Fajri Koto, et al.*2023 · 384 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-cmmlu-measuring-massive-multitask-language/)
59. [**A Survey on Model Compression for Large Language Models**Xunyu Zhu, Jian Li, Yong Liu, et al.*2024 · 381 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2024-survey-model-compression-large-language/)
60. [**LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Models**Neel Guha; Julian Nyarko; Daniel E. Ho; Christopher Re; Adam Chilton; Aditya Narayana; Alex Chohlas-Wood; Austin Peters; Brandon Waldon; Daniel N. Rockmore; Diego Zambrano; Dmitry Talisman; Enam Hoque; Faiz Surani; Frank Fagan; Galit Sarfaty; Gregory M. Dickinson; Haggai Porat; Jason Hegland; Jessica Wu; Joe Nudell; Joel Niklaus; John Nay; Jonathan H. Choi; Kevin Tobia; Margaret Hagan; Megan Ma; Michael Livermore; Nikon Rasumov-Rahe; Nils Holzenberger; Noam Kolt; Peter Henderson; Sean Rehaag; Sharad Goel; Shang Gao; Spencer Williams; Sunny Gandhi; Tom Zur; Varun Iyer;, Zehua Li*2023 · 379 citations*](https://md.research.elamur.ai/free/en/papers/guha-2023-legalbench-collaboratively-built-benchmark-measuring/)
61. [**Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning**Liangming Pan, Alon Albalak, Xinyi Wang, et al.*2023 · 370 citations*](https://md.research.elamur.ai/free/en/papers/pan-2023-logic-lm-empowering-large-language/)
62. [**What makes a good conversation? How controllable attributes affect human judgments**Abigail See, Stephen Roller, Douwe Kiela, et al.*2019 · 369 citations*](https://md.research.elamur.ai/free/en/papers/see-2019-what-makes-good-conversation-how/)
63. [**Augmented Language Models: a Survey**Grégoire Mialon, Roberto Dessì, Maria Lomeli, et al.*2023 · 362 citations*](https://md.research.elamur.ai/free/en/papers/mialon-2023-augmented-language-models-survey/)
64. [**Orca: Progressive Learning from Complex Explanation Traces of GPT-4**Subhabrata Mukherjee, Arindam Mitra, Ganesh Jawahar, et al.*2023 · 357 citations*](https://md.research.elamur.ai/free/en/papers/mukherjee-2023-orca-progressive-learning-complex-explanation/)
65. [**Unnatural Instructions: Tuning Language Models with (Almost) No Human Labor**Or Honovich, Thomas Scialom, Omer Levy, et al.*2023 · 356 citations*](https://md.research.elamur.ai/free/en/papers/honovich-2023-unnatural-instructions-tuning-language-models/)
66. [**QuestEval: Summarization Asks for Fact-based Evaluation**Thomas Scialom, Paul-Alexis Dray, Sylvain Lamprier, et al.*2021 · 353 citations*](https://md.research.elamur.ai/free/en/papers/scialom-2021-questeval-summarization-asks-fact-based/)
67. [**BeaverTails: Towards Improved Safety Alignment of LLM via a Human-Preference Dataset**Jiaming Ji, Mickel Liu, Juntao Dai, et al.*2023 · 351 citations*](https://md.research.elamur.ai/free/en/papers/ji-2023-beavertails-towards-improved-safety-alignment/)
68. [**ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate**Chi-Min Chan, Weize Chen, Yusheng Su, et al.*2023 · 351 citations*](https://md.research.elamur.ai/free/en/papers/chan-2023-chateval-better-llm-based/)
69. [**TinyLlama: An Open-Source Small Language Model**Peiyuan Zhang, Guangtao Zeng, Tianduo Wang, et al.*2024 · 350 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2024-tinyllama-open-source-small-language/)
70. [**FEQA: A Question Answering Evaluation Framework for Faithfulness Assessment in Abstractive Summarization**Esin Durmus, He He, Mona Diab*2020 · 350 citations*](https://md.research.elamur.ai/free/en/papers/durmus-2020-feqa-question-answering-evaluation-framework/)
71. [**Reformulating Unsupervised Style Transfer as Paraphrase Generation**Kalpesh Krishna, John Wieting, Mohit Iyyer*2020 · 348 citations*](https://md.research.elamur.ai/free/en/papers/krishna-2020-reformulating-unsupervised-style-transfer-as/)
72. [**UL2: Unifying Language Learning Paradigms**Yi Tay, Mostafa Dehghani, Vinh Q. Tran, et al.*2022 · 334 citations*](https://md.research.elamur.ai/free/en/papers/tay-2022-ul2-unifying-language-learning-paradigms/)
73. [**Qwen3-VL Technical Report**Shuai Bai, Yuxuan Cai, Ruizhe Chen, et al.*2025 · 330 citations*](https://md.research.elamur.ai/free/en/papers/bai-2025-qwen3-vl-technical-report/)
74. [**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/)
75. [**Faith and Fate: Limits of Transformers on Compositionality**Nouha Dziri, Ximing Lu, Melanie Sclar, et al.*2023 · 313 citations*](https://md.research.elamur.ai/free/en/papers/dziri-2023-faith-fate-limits-transformers-compositionality/)
76. [**WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation**Alisa Liu, Swabha Swayamdipta, Noah A. Smith, et al.*2022 · 312 citations*](https://md.research.elamur.ai/free/en/papers/liu-2022-wanli-worker-ai-collaboration-natural/)
77. [**Multi-hop Reading Comprehension through Question Decomposition and Rescoring**Sewon Min, Victor Zhong, Luke Zettlemoyer, et al.*2019 · 306 citations*](https://md.research.elamur.ai/free/en/papers/min-2019-multi-hop-reading-comprehension-through/)
78. [**Discovering Language Model Behaviors with Model-Written Evaluations**Ethan Perez, Sam Ringer, Kamilė Lukošiūtė, et al.*2022 · 305 citations*](https://md.research.elamur.ai/free/en/papers/perez-2022-discovering-language-model-behaviors-model/)
79. [**How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources**Yizhong Wang, Hamish Ivison, Pradeep Dasigi, et al.*2023 · 304 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-far-camels-go-exploring-state/)
80. [**Large Language Models are Better Reasoners with Self-Verification**Yixuan Weng, Minjun Zhu, Fei Xia, et al.*2023 · 302 citations*](https://md.research.elamur.ai/free/en/papers/weng-2023-large-language-models-are-better/)
81. [**Learning to Write with Cooperative Discriminators**Ari Holtzman, Jan Buys, Maxwell Forbes, et al.*2018 · 301 citations*](https://md.research.elamur.ai/free/en/papers/holtzman-2018-learning-write-cooperative-discriminators/)
82. [**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/)
83. [**Why We Need New Evaluation Metrics for NLG**Jekaterina Novikova, Ondřej Dušek, Amanda Cercas Curry, et al.*2017 · 287 citations*](https://md.research.elamur.ai/free/en/papers/novikova-2017-why-we-need-new-evaluation/)
84. [**MEGA: Multilingual Evaluation of Generative AI**Kabir Ahuja, Harshita Diddee, Rishav Hada, et al.*2023 · 281 citations*](https://md.research.elamur.ai/free/en/papers/ahuja-2023-mega-multilingual-evaluation-generative-ai/)
85. [**Learning Neural Templates for Text Generation**Sam Wiseman, Stuart Shieber, Alexander Rush*2018 · 279 citations*](https://md.research.elamur.ai/free/en/papers/wiseman-2018-learning-neural-templates-text-generation/)
86. [**Larger language models do in-context learning differently**Jerry Wei, Jason Wei, Yi Tay, et al.*2023 · 277 citations*](https://md.research.elamur.ai/free/en/papers/wei-2023-larger-language-models-context-learning/)
87. [**Hallucination-Free? Assessing the Reliability of Leading AI Legal Research Tools**Varun Magesh, Faiz Surani, Matthew Dahl, et al.*2024 · 267 citations*](https://md.research.elamur.ai/free/en/papers/magesh-2024-hallucination-free-assessing-reliability-leading/)
88. [**Towards Understanding Chain-of-Thought Prompting: An Empirical Study of What Matters**Boshi Wang, Sewon Min, Xiang Deng, et al.*2023 · 267 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-understanding-chain-thought-prompting-empirical/)
89. [**Handling Divergent Reference Texts when Evaluating Table-to-Text Generation**Bhuwan Dhingra, Manaal Faruqui, Ankur Parikh, et al.*2019 · 266 citations*](https://md.research.elamur.ai/free/en/papers/dhingra-2019-handling-divergent-reference-texts-when/)
90. [**Towards Measuring the Representation of Subjective Global Opinions in Language Models**Esin Durmus, Karina Nguyen, Thomas I. Liao, et al.*2023 · 262 citations*](https://md.research.elamur.ai/free/en/papers/durmus-2023-towards-measuring-representation-subjective-global/)
91. [**Large Language Models Sensitivity to The Order of Options in Multiple-Choice Questions**Pouya Pezeshkpour, Estevam Hruschka*2023 · 256 citations*](https://md.research.elamur.ai/free/en/papers/pezeshkpour-2023-large-language-models-sensitivity-order/)
92. [**Re-evaluating Evaluation in Text Summarization**Manik Bhandari, Pranav Narayan Gour, Atabak Ashfaq, et al.*2020 · 256 citations*](https://md.research.elamur.ai/free/en/papers/bhandari-2020-re-evaluating-evaluation-text-summarization/)
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