---
title: "Who cites Shashank Srivastava — 1,293 papers of our library"
description: "1,293 papers of the Elamur Research library cite the works of Shashank Srivastava, found in their bibliographies · page 1 of 2 · 1–100; the…"
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---
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# Who cites Shashank Srivastava

1,298 citations of their works from 1,293 papers of our library (of the 141,840 whose bibliographies we read) · 191 with a page here, the most cited first · page 1 of 2 · 1–100 · [← Shashank Srivastava](https://md.research.elamur.ai/free/en/authors/shashank-srivastava/)

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1. [**Gemini: A Family of Highly Capable Multimodal Models arXiv:2312.11805**Gemini Team, Rohan Anil, Sebastian Borgeaud, et al.*2023 · 2,957 citations*](https://md.research.elamur.ai/free/en/papers/gemini-team-2023-gemini-family-highly-capable-multimodal/) **[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 · 2,863 citations* [**Sparks of Artificial General Intelligence: Early experiments with GPT-4 arXiv:2303.12712**Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, et al.*2023 · 2,400 citations*](https://md.research.elamur.ai/free/en/papers/bubeck-2023-sparks-artificial-general-intelligence-early/) [**Emergent Abilities of Large Language Models arXiv:2206.07682**Jason Wei, Yi Tay, Rishi Bommasani, et al.*2022 · 2,229 citations*](https://md.research.elamur.ai/free/en/papers/wei-2022-emergent-abilities-large-language-models/) [**Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context arXiv:2403.05530**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/) **[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 · 1,286 citations* [**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 · 1,113 citations*](https://md.research.elamur.ai/free/en/papers/abdin-2024-phi-3-technical-report-highly/) **[Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them](https://md.research.elamur.ai/free/en/papers/suzgun-2023-challenging-big-bench-tasks-whether/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Mirac Suzgun, Nathan Scales, Nathanael Schärli, et al.*2023 · 1,096 citations* **[MTEB: Massive Text Embedding Benchmark](https://md.research.elamur.ai/free/en/papers/muennighoff-2022-mteb-massive-text-embedding-benchmark/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Niklas Muennighoff, Nouamane Tazi, Loïc Magne, et al.*2022 · 1,021 citations* [**PaLM 2 Technical Report arXiv:2305.10403**Rohan Anil, Andrew M. Dai, Orhan Firat, et al.*2023 · 977 citations*](https://md.research.elamur.ai/free/en/papers/anil-2023-palm-2-technical-report/) [**GPQA: A Graduate-Level Google-Proof Q&A Benchmark arXiv:2311.12022**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/) [**Constitutional AI: Harmlessness from AI Feedback arXiv:2212.08073**Yuntao Bai, Saurav Kadavath, Sandipan Kundu, et al.*2022 · 962 citations*](https://md.research.elamur.ai/free/en/papers/bai-2022-constitutional-ai-harmlessness-ai-feedback/) [**A Survey on In-context Learning arXiv:2301.00234**Qingxiu Dong, Lei Li, Damai Dai, et al.*2023 · 959 citations*](https://md.research.elamur.ai/free/en/papers/dong-2023-survey-context-learning/) **[C-Pack: Packed Resources For General Chinese Embeddings](https://md.research.elamur.ai/free/en/papers/xiao-2023-c-pack-packed-resources/) [International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)](https://md.research.elamur.ai/free/en/venues/international-acm-sigir-conference-on-research-and-development-in-information/)**Shitao Xiao, Zheng Liu, Peitian Zhang, et al.*2023 · 920 citations* [**A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity arXiv:2302.04023**Yejin Bang, Samuel Cahyawijaya, Nayeon Lee, et al.*2023 · 876 citations*](https://md.research.elamur.ai/free/en/papers/bang-2023-multitask-multilingual-multimodal-evaluation-chatgpt/) [**ChatGPT: Jack of all trades, master of none arXiv:2302.10724 · Information Fusion 101,861 (2023)**Jan Kocon, Igor Cichecki, Oliwier Kaszyca, et al.*2023 · 865 citations*](https://md.research.elamur.ai/free/en/papers/kocon-2023-chatgpt-jack-all-trades-master/) **[Is ChatGPT a General-Purpose Natural Language Processing Task Solver?](https://md.research.elamur.ai/free/en/papers/qin-2023-chatgpt-general-purpose-natural-language/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Chengwei Qin, Aston Zhang, Zhuosheng Zhang, et al.*2023 · 823 citations* [**Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond arXiv:2304.13712**Jingfeng Yang, Hongye Jin, Ruixiang Tang, et al.*2023 · 811 citations*](https://md.research.elamur.ai/free/en/papers/yang-2023-harnessing-power-llms-practice-survey/) [**GLM-130B: An Open Bilingual Pre-trained Model arXiv:2210.02414**Aohan Zeng, Xiao Liu, Zhengxiao Du, et al.*2022 · 779 citations*](https://md.research.elamur.ai/free/en/papers/zeng-2022-glm-130b-open-bilingual-pre/) **[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 · 778 citations* **[Can Large Language Models Transform Computational Social Science?](https://md.research.elamur.ai/free/en/papers/ziems-2023-large-language-models-transform-computational/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Caleb Ziems, William Held, Omar Shaikh, et al.*2023 · 777 citations* **[Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning](https://md.research.elamur.ai/free/en/papers/liu-2022-few-shot-parameter-efficient-fine/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Haokun Liu, Derek Tam, Mohammed Muqeeth, et al.*2022 · 736 citations* **[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 · 731 citations* [**Crosslingual Generalization through Multitask Finetuning arXiv:2211.01786**Niklas Muennighoff, Thomas Wang, Lintang Sutawika, et al.*2022 · 695 citations*](https://md.research.elamur.ai/free/en/papers/muennighoff-2022-crosslingual-generalization-through-multitask-finetuning/) [**ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools arXiv:2406.12793**Team GLM, Aohan Zeng, Bin Xu, et al.*2024 · 688 citations*](https://md.research.elamur.ai/free/en/papers/glm-2024-chatglm-family-large-language-models/) [**Large Language Models Are Human-Level Prompt Engineers arXiv:2211.01910**Yongchao Zhou, Andrei Ioan Muresanu, Ziwen Han, et al.*2022 · 678 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2022-large-language-models-human-level/) **[Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate](https://md.research.elamur.ai/free/en/papers/liang-2023-encouraging-divergent-thinking-large-language/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Tian Liang, Zhiwei He, Wenxiang Jiao, et al.*2023 · 643 citations* [**SWE-bench: Can Language Models Resolve Real-World GitHub Issues? arXiv:2310.06770**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/) [**BloombergGPT: A Large Language Model for Finance arXiv:2303.17564**Shijie Wu, Ozan Irsoy, Steven Lu, et al.*2023 · 612 citations*](https://md.research.elamur.ai/free/en/papers/wu-2023-bloomberggpt-large-language-model-finance/) [**The Flan Collection: Designing Data and Methods for Effective Instruction Tuning arXiv:2301.13688**Shayne Longpre, Le Hou, Tu Vu, et al.*2023 · 592 citations*](https://md.research.elamur.ai/free/en/papers/longpre-2023-flan-collection-designing-data-methods/) **[s1: Simple test-time scaling](https://md.research.elamur.ai/free/en/papers/muennighoff-2025-s1-simple-test-time-scaling/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Niklas Muennighoff, Zitong Yang, Weijia Shi, et al.*2025 · 584 citations* **[Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting](https://md.research.elamur.ai/free/en/papers/turpin-2023-language-models-don-t-always/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Miles Turpin, Julian Michael, Ethan Perez, et al.*2023 · 568 citations* [**Improving Factuality and Reasoning in Language Models through Multiagent Debate arXiv:2305.14325**Yilun Du, Shuang Li, Antonio Torralba, et al.*2023 · 565 citations*](https://md.research.elamur.ai/free/en/papers/du-2023-improving-factuality-reasoning-language-models/) **[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 · 563 citations* [**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/) **[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 · 517 citations* [**Baichuan 2: Open Large-scale Language Models arXiv:2309.10305**Aiyuan Yang, Bin Xiao, Bingning Wang, et al.*2023 · 513 citations*](https://md.research.elamur.ai/free/en/papers/yang-2023-baichuan-2-open-large-scale/) [**LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding arXiv:2308.14508**Yushi Bai, Xin Lv, Jiajie Zhang, et al.*2023 · 506 citations*](https://md.research.elamur.ai/free/en/papers/bai-2023-longbench-bilingual-multitask-benchmark/) [**The Falcon Series of Open Language Models arXiv:2311.16867**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/) [**C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models arXiv:2305.08322**Yuzhen Huang, Yuzhuo Bai, Zhihao Zhu, et al.*2023 · 428 citations*](https://md.research.elamur.ai/free/en/papers/huang-2023-c-eval-multi-level-multi/) [**AgentBench: Evaluating LLMs as Agents arXiv:2308.03688**Xiao Liu, Hao Yu, Hanchen Zhang, et al.*2023 · 405 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-agentbench-evaluating-llms-agents/) [**True Few-Shot Learning with Language Models arXiv:2105.11447**Ethan Perez, Douwe Kiela, Kyunghyun Cho*2021 · 404 citations*](https://md.research.elamur.ai/free/en/papers/perez-2021-true-few-shot-learning-language/) [**Yi: Open Foundation Models by 01.AI arXiv:2403.04652**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/) **[Do Prompt-Based Models Really Understand the Meaning of Their Prompts?](https://md.research.elamur.ai/free/en/papers/webson-2022-do-prompt-based-models-really/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Albert Webson, Ellie Pavlick*2022 · 398 citations* [**Whose Opinions Do Language Models Reflect? arXiv:2303.17548**Shibani Santurkar, Esin Durmus, Faisal Ladhak, et al.*2023 · 392 citations*](https://md.research.elamur.ai/free/en/papers/santurkar-2023-whose-opinions-language-models-reflect/) [**Large Language Models as Optimizers arXiv:2309.03409**Chengrun Yang, Xuezhi Wang, Yifeng Lu, et al.*2023 · 390 citations*](https://md.research.elamur.ai/free/en/papers/yang-2023-large-language-models-optimizers/) [**CMMLU: Measuring massive multitask language understanding in Chinese arXiv:2306.09212**Haonan Li, Yixuan Zhang, Fajri Koto, et al.*2023 · 385 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-cmmlu-measuring-massive-multitask-language/) **[A Survey on Model Compression for Large Language Models](https://md.research.elamur.ai/free/en/papers/zhu-2024-survey-model-compression-large-language/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Xunyu Zhu, Jian Li, Yong Liu, et al.*2024 · 381 citations* [**LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Models arXiv:2308.11462**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 · 381 citations*](https://md.research.elamur.ai/free/en/papers/guha-2023-legalbench-collaboratively-built-benchmark-measuring/) [**Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning arXiv:2305.12295**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/) [**Augmented Language Models: a Survey arXiv:2302.07842**Grégoire Mialon, Roberto Dessì, Maria Lomeli, et al.*2023 · 366 citations*](https://md.research.elamur.ai/free/en/papers/mialon-2023-augmented-language-models-survey/) [**Orca: Progressive Learning from Complex Explanation Traces of GPT-4 arXiv:2306.02707**Subhabrata Mukherjee, Arindam Mitra, Ganesh Jawahar, et al.*2023 · 358 citations*](https://md.research.elamur.ai/free/en/papers/mukherjee-2023-orca-progressive-learning-complex-explanation/) **[Unnatural Instructions: Tuning Language Models with (Almost) No Human Labor](https://md.research.elamur.ai/free/en/papers/honovich-2023-unnatural-instructions-tuning-language-models/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Or Honovich, Thomas Scialom, Omer Levy, et al.*2023 · 358 citations* **[BeaverTails: Towards Improved Safety Alignment of LLM via a Human-Preference Dataset](https://md.research.elamur.ai/free/en/papers/ji-2023-beavertails-towards-improved-safety-alignment/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Jiaming Ji, Mickel Liu, Juntao Dai, et al.*2023 · 352 citations* [**TinyLlama: An Open-Source Small Language Model arXiv:2401.02385**Peiyuan Zhang, Guangtao Zeng, Tianduo Wang, et al.*2024 · 351 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2024-tinyllama-open-source-small-language/) [**UL2: Unifying Language Learning Paradigms arXiv:2205.05131**Yi Tay, Mostafa Dehghani, Vinh Q. Tran, et al.*2022 · 335 citations*](https://md.research.elamur.ai/free/en/papers/tay-2022-ul2-unifying-language-learning-paradigms/) **[ProofWriter: Generating Implications, Proofs, and Abductive Statements over Natural Language](https://md.research.elamur.ai/free/en/papers/tafjord-2021-proofwriter-generating-implications-proofs-abductive/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Oyvind Tafjord, Bhavana Dalvi, Peter Clark*2021 · 331 citations* **[RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/deng-2022-rlprompt-optimizing-discrete-text-prompts/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Mingkai Deng, Jianyu Wang, Cheng-Ping Hsieh, et al.*2022 · 328 citations* **[Are Emergent Abilities of Large Language Models a Mirage?](https://md.research.elamur.ai/free/en/papers/schaeffer-2023-emergent-abilities-large-language-models/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Rylan Schaeffer, Brando Miranda, Sanmi Koyejo*2023 · 323 citations* [**WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation arXiv:2201.05955**Alisa Liu, Swabha Swayamdipta, Noah A. Smith, et al.*2022 · 314 citations*](https://md.research.elamur.ai/free/en/papers/liu-2022-wanli-worker-ai-collaboration-natural/) **[Faith and Fate: Limits of Transformers on Compositionality](https://md.research.elamur.ai/free/en/papers/dziri-2023-faith-fate-limits-transformers-compositionality/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Nouha Dziri, Ximing Lu, Melanie Sclar, et al.*2023 · 313 citations* [**Discovering Language Model Behaviors with Model-Written Evaluations arXiv:2212.09251**Ethan Perez, Sam Ringer, Kamilė Lukošiūtė, et al.*2022 · 307 citations*](https://md.research.elamur.ai/free/en/papers/perez-2022-discovering-language-model-behaviors-model/) **[How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources](https://md.research.elamur.ai/free/en/papers/wang-2023-far-camels-go-exploring-state/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Yizhong Wang, Hamish Ivison, Pradeep Dasigi, et al.*2023 · 304 citations* **[Large Language Models are Better Reasoners with Self-Verification](https://md.research.elamur.ai/free/en/papers/weng-2023-large-language-models-are-better/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yixuan Weng, Minjun Zhu, Fei Xia, et al.*2023 · 302 citations* [**Challenges and Applications of Large Language Models arXiv:2307.10169**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/) [**MEGA: Multilingual Evaluation of Generative AI arXiv:2303.12528**Kabir Ahuja, Harshita Diddee, Rishav Hada, et al.*2023 · 282 citations*](https://md.research.elamur.ai/free/en/papers/ahuja-2023-mega-multilingual-evaluation-generative-ai/) [**Larger language models do in-context learning differently arXiv:2303.03846**Jerry Wei, Jason Wei, Yi Tay, et al.*2023 · 279 citations*](https://md.research.elamur.ai/free/en/papers/wei-2023-larger-language-models-context-learning/) **[Towards Understanding Chain-of-Thought Prompting: An Empirical Study of What Matters](https://md.research.elamur.ai/free/en/papers/wang-2023-understanding-chain-thought-prompting-empirical/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Boshi Wang, Sewon Min, Xiang Deng, et al.*2023 · 269 citations* [**Hallucination-Free? Assessing the Reliability of Leading AI Legal Research Tools arXiv:2405.20362**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/) [**Towards Measuring the Representation of Subjective Global Opinions in Language Models arXiv:2306.16388**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/) **[Large Language Models Sensitivity to The Order of Options in Multiple-Choice Questions](https://md.research.elamur.ai/free/en/papers/pezeshkpour-2023-large-language-models-sensitivity-order/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Pouya Pezeshkpour, Estevam Hruschka*2023 · 256 citations* **[Generating Datasets with Pretrained Language Models](https://md.research.elamur.ai/free/en/papers/schick-2021-generating-datasets-pretrained-language-models/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Timo Schick, Hinrich Schutze*2021 · 250 citations* **[DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT 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