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title: "Who cites Christopher E. Potts — page 3 — 9,352 citing papers"
description: "9,352 papers of the Elamur Research library cite the works of Christopher E. Among them: s1: Simple test-time scaling Conference on…"
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# Who cites Christopher E. Potts

11,943 citations of their works from 9,352 papers of our library (of the 149,439 whose bibliographies we read) · 1,871 with a page here, the most cited first · page 3 of 19 · 201–300 · [← Christopher E. Potts](https://md.research.elamur.ai/free/en/authors/christopher-e-potts/)

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201. **[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 · 608 citations*
202. [**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/)
203. [**Text Embeddings by Weakly-Supervised Contrastive Pre-training arXiv:2212.03533**Liang Wang, Nan Yang, Xiaolong Huang, et al.*2022 · 598 citations*](https://md.research.elamur.ai/free/en/papers/wang-2022-text-embeddings-weakly-supervised-contrastive/)
204. **[Efficient Non-parametric Estimation of Multiple Embeddings per Word in Vector Space](https://md.research.elamur.ai/free/en/papers/neelakantan-2014-efficient-non-parametric-estimation-multiple/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Arvind Neelakantan, Jeevan Shankar, Alexandre Passos, et al.*2014 · 598 citations*
205. **[Fine-grained Opinion Mining with Recurrent Neural Networks and Word Embeddings](https://md.research.elamur.ai/free/en/papers/liu-2015-fine-grained-opinion-mining-recurrent/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Pengfei Liu, Shafiq Joty, Helen Meng*2015 · 593 citations*
206. **[Relation Extraction: Perspective from Convolutional Neural Networks](https://md.research.elamur.ai/free/en/papers/nguyen-2015-relation-extraction-perspective-convolutional-neural/) [Workshop on Vector Space Modeling for Natural Language Processing](https://md.research.elamur.ai/free/en/venues/workshop-on-vector-space-modeling-for-natural-language-processing/)**Thien Huu Nguyen, Ralph Grishman*2015 · 592 citations*
207. [**Improving Factuality and Reasoning in Language Models through Multiagent Debate arXiv:2305.14325**Yilun Du, Shuang Li, Antonio Torralba, et al.*2023 · 587 citations*](https://md.research.elamur.ai/free/en/papers/du-2023-improving-factuality-reasoning-language-models/)
208. **[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 · 586 citations*
209. **[Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Models](https://md.research.elamur.ai/free/en/papers/ni-2021-sentence-t5-scalable-sentence-encoders/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jianmo Ni, Gustavo Hernandez Abrego, Noah Constant, et al.*2021 · 584 citations*
210. [**Plug and Play Language Models: A Simple Approach to Controlled Text Generation arXiv:1912.02164**Sumanth Dathathri, Andrea Madotto, Janice Lan, et al.*2019 · 578 citations*](https://md.research.elamur.ai/free/en/papers/dathathri-2019-plug-play-language-models-simple/)
211. [**An efficient framework for learning sentence representations arXiv:1803.02893**Lajanugen Logeswaran, Honglak Lee*2018 · 577 citations*](https://md.research.elamur.ai/free/en/papers/logeswaran-2018-efficient-framework-learning-sentence-representations/)
212. **[AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models](https://md.research.elamur.ai/free/en/papers/zhong-2024-agieval-human-centric-benchmark-evaluating/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Wanjun Zhong, Ruixiang Cui, Yiduo Guo, et al.*2024 · 575 citations*
213. **[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*
214. **[Multilingual Universal Sentence Encoder for Semantic Retrieval](https://md.research.elamur.ai/free/en/papers/yang-2020-multilingual-universal-sentence-encoder-semantic/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yinfei Yang, Daniel Cer, Amin Ahmad, et al.*2020 · 570 citations*
215. [**Reducing Transformer Depth on Demand with Structured Dropout arXiv:1909.11556**Angela Fan, Edouard Grave, Armand Joulin*2019 · 560 citations*](https://md.research.elamur.ai/free/en/papers/fan-2019-reducing-transformer-depth-demand-structured/)
216. [**A Categorical Archive of ChatGPT Failures arXiv:2302.03494**Ali Borji*2023 · 559 citations*](https://md.research.elamur.ai/free/en/papers/borji-2023-categorical-archive-chatgpt-failures/)
217. [**Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference arXiv:2403.04132**Wei-Lin Chiang, Lianmin Zheng, Ying Sheng, et al.*2024 · 546 citations*](https://md.research.elamur.ai/free/en/papers/chiang-2024-chatbot-arena-open-platform-evaluating/)
218. [**TextBugger: Generating Adversarial Text Against Real-world Applications arXiv:1812.05271**Jinfeng Li, Shouling Ji, Tianyu Du, et al.*2018 · 545 citations*](https://md.research.elamur.ai/free/en/papers/li-2018-textbugger-generating-adversarial-text-against/)
219. [**The Natural Language Decathlon: Multitask Learning as Question Answering arXiv:1806.08730**Bryan McCann, Nitish Shirish Keskar, Caiming Xiong, et al.*2018 · 545 citations*](https://md.research.elamur.ai/free/en/papers/mccann-2018-natural-language-decathlon-multitask-learning/)
220. **[Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning](https://md.research.elamur.ai/free/en/papers/aghajanyan-2021-intrinsic-dimensionality-explains-effectiveness-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Armen Aghajanyan, Sonal Gupta, Luke Zettlemoyer*2021 · 542 citations*
221. [**Toward Controlled Generation of Text arXiv:1703.00955**Zhiting Hu, Zichao Yang, Xiaodan Liang, et al.*2017 · 537 citations*](https://md.research.elamur.ai/free/en/papers/hu-2017-toward-controlled-generation-text/)
222. [**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/)
223. **[SpAtten: Efficient Sparse Attention Architecture with Cascade Token and Head Pruning](https://md.research.elamur.ai/free/en/papers/wang-2020-spatten-efficient-sparse-attention-architecture/) [Proceedings - International Symposium on High-Performance Computer Architecture/Proceedings](https://md.research.elamur.ai/free/en/venues/proceedings-international-symposium-on-high-performance-computer-architecture/)**Hanrui Wang, Zhekai Zhang, Song Han*2020 · 534 citations*
224. [**Evaluating Models' Local Decision Boundaries via Contrast Sets**Matt Gardner, Yoav Artzi, Victoria Basmov, et al.*2020 · 531 citations*](https://md.research.elamur.ai/free/en/papers/gardner-2020-evaluating-models-local-decision-boundaries/)
225. **[The LAMBADA dataset: Word prediction requiring a broad discourse context](https://md.research.elamur.ai/free/en/papers/paperno-2016-lambada-dataset-word-prediction-requiring/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Denis Paperno, Germán Kruszewski, Angeliki Lazaridou, et al.*2016 · 528 citations*
226. [**Breaking NLI Systems with Sentences that Require Simple Lexical Inferences**Max Glockner, Vered Shwartz, Yoav Goldberg*2018 · 525 citations*](https://md.research.elamur.ai/free/en/papers/glockner-2018-breaking-nli-systems-sentences-that/)
227. **[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*
228. **[Fact Checking: Task definition and dataset construction](https://md.research.elamur.ai/free/en/papers/vlachos-2014-fact-checking-task-definition-dataset/) [ACL Workshop on Language Technologies and Computational Social Science](https://md.research.elamur.ai/free/en/venues/acl-workshop-on-language-technologies-and-computational-social-science/)**Andreas Vlachos, Sebastian Riedel*2014 · 524 citations*
229. **[To Tune or Not to Tune? Adapting Pretrained Representations to Diverse Tasks](https://md.research.elamur.ai/free/en/papers/peters-2019-tune-tune-adapting-pretrained-representations/) [Workshop on Representation Learning for NLP](https://md.research.elamur.ai/free/en/venues/repl4nlp/)**Matthew E. Peters, Sebastian Ruder, Noah A. Smith*2019 · 522 citations*
230. **[Stance Detection with Bidirectional Conditional Encoding](https://md.research.elamur.ai/free/en/papers/augenstein-2016-stance-detection-bidirectional-conditional-encoding/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Isabelle Augenstein, Tim Rocktäschel, Andreas Vlachos, et al.*2016 · 522 citations*
231. **[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*
232. **[Learning Gender-Neutral Word Embeddings](https://md.research.elamur.ai/free/en/papers/zhao-2018-learning-gender-neutral-word-embeddings/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jieyu Zhao, Yichao Zhou, Zeyu Li, et al.*2018 · 520 citations*
233. [**Baichuan 2: Open Large-scale Language Models arXiv:2309.10305**Aiyuan Yang, Bin Xiao, Bingning Wang, et al.*2023 · 517 citations*](https://md.research.elamur.ai/free/en/papers/yang-2023-baichuan-2-open-large-scale/)
234. **[DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations](https://md.research.elamur.ai/free/en/papers/giorgi-2021-declutr-deep-contrastive-learning-unsupervised/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**John Giorgi, Osvald Nitski, Bo Wang, et al.*2021 · 516 citations*
235. **[Recursive Neural Conditional Random Fields for Aspect-based Sentiment Analysis](https://md.research.elamur.ai/free/en/papers/wang-2016-recursive-neural-conditional-random-fields/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Wenya Wang, Sinno Jialin Pan, Daniel Dahlmeier, et al.*2016 · 516 citations*
236. [**TabFact: A Large-scale Dataset for Table-based Fact Verification arXiv:1909.02164**Wenhu Chen, Hongmin Wang, Jianshu Chen, et al.*2019 · 515 citations*](https://md.research.elamur.ai/free/en/papers/chen-2019-tabfact-large-scale-dataset-table/)
237. [**LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding arXiv:2308.14508**Yushi Bai, Xin Lv, Jiajie Zhang, et al.*2023 · 514 citations*](https://md.research.elamur.ai/free/en/papers/bai-2023-longbench-bilingual-multitask-benchmark/)
238. [**Learned in Translation: Contextualized Word Vectors arXiv:1708.00107**Bryan McCann, James Bradbury, Caiming Xiong, et al.*2017 · 512 citations*](https://md.research.elamur.ai/free/en/papers/mccann-2017-learned-translation-contextualized-word-vectors/)
239. [**Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time arXiv:2203.05482**Mitchell Wortsman, Gabriel Ilharco, Samir Yitzhak Gadre, et al.*2022 · 507 citations*](https://md.research.elamur.ai/free/en/papers/wortsman-2022-model-soups-averaging-weights-multiple/)
240. **[Sentiment Analysis in the Era of Large Language Models: A Reality Check](https://md.research.elamur.ai/free/en/papers/zhang-2023-sentiment-analysis-era-large-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Wenxuan Zhang, Yue Deng, Bing Liu, et al.*2023 · 506 citations*
241. **[Does String-Based Neural MT Learn Source Syntax?](https://md.research.elamur.ai/free/en/papers/shi-2016-does-string-based-neural-mt/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Xing Shi, Inkit Padhi, Kevin Knight*2016 · 504 citations*
242. [**Sentence Encoders on STILTs: Supplementary Training on Intermediate Labeled-data Tasks arXiv:1811.01088**Jason Phang, Thibault Févry, Samuel R. Bowman*2018 · 503 citations*](https://md.research.elamur.ai/free/en/papers/phang-2018-sentence-encoders-stilts-supplementary-training/)
243. [**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/)
244. **[Learning Natural Language Inference with LSTM](https://md.research.elamur.ai/free/en/papers/wang-2016-learning-natural-language-inference-lstm/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Shuohang Wang, Jing Jiang*2016 · 499 citations*
245. [**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/)
246. [**REPLUG: Retrieval-Augmented Black-Box Language Models arXiv:2301.12652**Weijia Shi, Sewon Min, Michihiro Yasunaga, et al.*2023 · 492 citations*](https://md.research.elamur.ai/free/en/papers/shi-2023-replug-retrieval-augmented-black-box/)
247. [**DyNet: The Dynamic Neural Network Toolkit arXiv:1701.03980**Graham Neubig, Chris Dyer, Yoav Goldberg, et al.*2017 · 489 citations*](https://md.research.elamur.ai/free/en/papers/neubig-2017-dynet-dynamic-neural-network-toolkit/)
248. **[EmoNet: Fine-Grained Emotion Detection with Gated Recurrent Neural Networks](https://md.research.elamur.ai/free/en/papers/mageed-2017-emonet-fine-grained-emotion-detection/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Muhammad Abdul-Mageed, Lyle Ungar*2017 · 487 citations*
249. **[Enabling Large Language Models to Generate Text with Citations](https://md.research.elamur.ai/free/en/papers/gao-2023-enabling-large-language-models-generate/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Tianyu Gao, Howard Yen, Jiatong Yu, et al.*2023 · 485 citations*
250. **[ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction](https://md.research.elamur.ai/free/en/papers/santhanam-2022-colbertv2-effective-efficient-retrieval-lightweight/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Keshav Santhanam, Omar Khattab, Jon Saad-Falcon, et al.*2022 · 485 citations*
251. [**Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned arXiv:2209.07858**Deep Ganguli, Liane Lovitt, Jackson Kernion, et al.*2022 · 485 citations*](https://md.research.elamur.ai/free/en/papers/ganguli-2022-red-teaming-language-models-reduce/)
252. [**SimVLM: Simple Visual Language Model Pretraining with Weak Supervision arXiv:2108.10904**Zirui Wang, Jiahui Yu, Adams Wei Yu, et al.*2021 · 481 citations*](https://md.research.elamur.ai/free/en/papers/wang-2021-simvlm-simple-visual-language-model/)
253. **[HERO: Hierarchical Encoder for Video+Language Omni-representation Pre-training](https://md.research.elamur.ai/free/en/papers/li-2020-hero-hierarchical-encoder-video-language/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Linjie Li, Yen-Chun Chen, Yu Cheng, et al.*2020 · 481 citations*
254. [**Data Augmentation using Pre-trained Transformer Models arXiv:2003.02245**Varun Kumar, Ashutosh Choudhary, Eunah Cho*2020 · 474 citations*](https://md.research.elamur.ai/free/en/papers/kumar-2020-data-augmentation-using-pre-trained/)
255. [**Learning to Generate Reviews and Discovering Sentiment arXiv:1704.01444**Alec Radford, Rafal Jozefowicz, Ilya Sutskever*2017 · 474 citations*](https://md.research.elamur.ai/free/en/papers/radford-2017-learning-generate-reviews-discovering-sentiment/)
256. **[PPT: Pre-trained Prompt Tuning for Few-shot Learning](https://md.research.elamur.ai/free/en/papers/gu-2022-ppt-pre-trained-prompt-tuning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yuxian Gu, Xu Han, Zhiyuan Liu, et al.*2022 · 471 citations*
257. **[Representation Learning Using Multi-Task Deep Neural Networks for Semantic Classification and Information Retrieval](https://md.research.elamur.ai/free/en/papers/liu-2015-representation-learning-multi-task-deep/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Xiaodong Liu, Jianfeng Gao, Xiaodong He, et al.*2015 · 471 citations*
258. [**MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts arXiv:2310.02255**Pan Lu, Hritik Bansal, Tony Xia, et al.*2023 · 468 citations*](https://md.research.elamur.ai/free/en/papers/lu-2023-mathvista-evaluating-mathematical-reasoning-foundation/)
259. **[LinkBERT: Pretraining Language Models with Document Links](https://md.research.elamur.ai/free/en/papers/yasunaga-2022-linkbert-pretraining-language-models-document/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Michihiro Yasunaga, Jure Leskovec, Percy Liang*2022 · 458 citations*
260. **[Emergent: a novel data-set for stance classification](https://md.research.elamur.ai/free/en/papers/ferreira-2016-emergent-novel-data-set-stance/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**William Ferreira, Andreas Vlachos*2016 · 458 citations*
261. **[AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback](https://md.research.elamur.ai/free/en/papers/dubois-2023-alpacafarm-simulation-framework-methods-that/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Yann Dubois, Xuechen Li, Rohan Taori, et al.*2023 · 456 citations*
262. [**Ethical and social risks of harm from Language Models arXiv:2112.04359**Laura Weidinger, John Mellor, Maribeth Rauh, et al.*2021 · 456 citations*](https://md.research.elamur.ai/free/en/papers/weidinger-2021-ethical-social-risks-harm-language/)
263. **[Bilingual Word Representations with Monolingual Quality in Mind](https://md.research.elamur.ai/free/en/papers/luong-2015-bilingual-word-representations-monolingual-quality/) [Workshop on Vector Space Modeling for Natural Language Processing](https://md.research.elamur.ai/free/en/venues/workshop-on-vector-space-modeling-for-natural-language-processing/)**Thang Luong, Hieu Pham, Christopher D. Manning*2015 · 456 citations*
264. [**Inherent Disagreements in Human Textual Inferences**Ellie Pavlick, Tom Kwiatkowski*2019 · 454 citations*](https://md.research.elamur.ai/free/en/papers/pavlick-2019-inherent-disagreements-human-textual-inferences/)
265. **[Pretrained Transformers Improve Out-of-Distribution Robustness](https://md.research.elamur.ai/free/en/papers/hendrycks-2020-pretrained-transformers-improve-out-distribution/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Dan Hendrycks, Xiaoyuan Liu, Eric Wallace, et al.*2020 · 452 citations*
266. [**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/)
267. **[GeDi: Generative Discriminator Guided Sequence Generation](https://md.research.elamur.ai/free/en/papers/krause-2021-gedi-generative-discriminator-guided-sequence/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Ben Krause, Akhilesh Deepak Gotmare, Bryan McCann, et al.*2021 · 449 citations*
268. **[Ranking Generated Summaries by Correctness: An Interesting but Challenging Application for Natural Language Inference](https://md.research.elamur.ai/free/en/papers/falke-2019-ranking-generated-summaries-correctness-interesting/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Tobias Falke, Leonardo F. R. Ribeiro, Prasetya Ajie Utama, et al.*2019 · 445 citations*
269. [**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/)
270. [**Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents arXiv:2201.07207**Wenlong Huang, Pieter Abbeel, Deepak Pathak, et al.*2022 · 443 citations*](https://md.research.elamur.ai/free/en/papers/huang-2022-language-models-zero-shot-planners/)
271. **[Improving Text Embeddings with Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2024-improving-text-embeddings-large-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Liang Wang, Nan Yang, Xiaolong Huang, et al.*2024 · 437 citations*
272. [**Large Legal Fictions: Profiling Legal Hallucinations in Large Language Models arXiv:2401.01301 · Journal of Legal Analysis 16, no. 1 (2024): 64-93**Matthew Dahl, Varun Magesh, Mirac Suzgun, et al.*2024 · 437 citations*](https://md.research.elamur.ai/free/en/papers/dahl-2024-large-legal-fictions-profiling-legal/)
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