---
title: "Who cites Christopher E. Potts — page 4 — 9,352 citing papers"
description: "9,352 papers of the Elamur Research library cite the works of Christopher E. Among them: Combating Adversarial Misspellings with Robust…"
canonical: "https://research.elamur.ai/free/en/authors/christopher-e-potts/cited-by/page/4/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/christopher-e-potts/cited-by/page/4/)

[home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / [Christopher E. Potts](https://md.research.elamur.ai/free/en/authors/christopher-e-potts/) / [cited by](https://md.research.elamur.ai/free/en/authors/christopher-e-potts/cited-by/) / page 4

# 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 4 of 19 · 301–400 · [← Christopher E. Potts](https://md.research.elamur.ai/free/en/authors/christopher-e-potts/)

[← prev](https://md.research.elamur.ai/free/en/authors/christopher-e-potts/cited-by/page/3/) [1](https://md.research.elamur.ai/free/en/authors/christopher-e-potts/cited-by/) [2](https://md.research.elamur.ai/free/en/authors/christopher-e-potts/cited-by/page/2/) [3](https://md.research.elamur.ai/free/en/authors/christopher-e-potts/cited-by/page/3/) **4** [5](https://md.research.elamur.ai/free/en/authors/christopher-e-potts/cited-by/page/5/) … [10](https://md.research.elamur.ai/free/en/authors/christopher-e-potts/cited-by/page/10/) … [19](https://md.research.elamur.ai/free/en/authors/christopher-e-potts/cited-by/page/19/) [next →](https://md.research.elamur.ai/free/en/authors/christopher-e-potts/cited-by/page/5/)

301. **[Combating Adversarial Misspellings with Robust Word Recognition](https://md.research.elamur.ai/free/en/papers/pruthi-2019-combating-adversarial-misspellings-robust-word/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Danish Pruthi, Bhuwan Dhingra, Zachary C. Lipton*2019 · 390 citations*
302. [**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 · 389 citations*](https://md.research.elamur.ai/free/en/papers/guha-2023-legalbench-collaboratively-built-benchmark-measuring/)
303. [**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/)
304. **[Dialogue Natural Language Inference](https://md.research.elamur.ai/free/en/papers/welleck-2019-dialogue-natural-language-inference/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Sean Welleck, Jason Weston, Arthur Szlam, et al.*2019 · 385 citations*
305. **[Query Rewriting in Retrieval-Augmented Large Language Models](https://md.research.elamur.ai/free/en/papers/ma-2023-query-rewriting-retrieval-augmented-large/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Xinbei Ma, Yeyun Gong, Pengcheng He, et al.*2023 · 384 citations*
306. [**Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergy arXiv:2305.15294**Zhihong Shao, Yeyun Gong, Yelong Shen, et al.*2023 · 383 citations*](https://md.research.elamur.ai/free/en/papers/shao-2023-enhancing-retrieval-augmented-large-language/)
307. **[Tool Learning with Foundation Models](https://md.research.elamur.ai/free/en/papers/qin-2023-tool-learning-foundation-models/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Yujia Qin, Shengding Hu, Yankai Lin, et al.*2023 · 383 citations*
308. **[Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity](https://md.research.elamur.ai/free/en/papers/jeong-2024-adaptive-rag-learning-adapt-retrieval/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Soyeong Jeong, Jinheon Baek, Sukmin Cho, et al.*2024 · 382 citations*
309. **[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*
310. **[From Paraphrase Database to Compositional Paraphrase Model and Back](https://md.research.elamur.ai/free/en/papers/wieting-2015-paraphrase-database-compositional-paraphrase-model/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**John Wieting, Mohit Bansal, Kevin Gimpel, et al.*2015 · 379 citations*
311. [**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 · 374 citations*](https://md.research.elamur.ai/free/en/papers/pan-2023-logic-lm-empowering-large-language/)
312. **[A Latent Variable Model Approach to PMI-based Word Embeddings](https://md.research.elamur.ai/free/en/papers/arora-2016-latent-variable-model-approach-pmi/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Sanjeev Arora, Yuanzhi Li, Yingyu Liang, et al.*2016 · 372 citations*
313. **[Improving Lexical Embeddings with Semantic Knowledge](https://md.research.elamur.ai/free/en/papers/yu-2014-improving-lexical-embeddings-semantic-knowledge/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mo Yu, Mark Dredze*2014 · 371 citations*
314. **[Political Ideology Detection Using Recursive Neural Networks](https://md.research.elamur.ai/free/en/papers/iyyer-2014-political-ideology-detection-recursive-neural/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mohit Iyyer, Peter Enns, Jordan Boyd-Graber, et al.*2014 · 370 citations*
315. [**Augmented Language Models: a Survey arXiv:2302.07842**Grégoire Mialon, Roberto Dessì, Maria Lomeli, et al.*2023 · 369 citations*](https://md.research.elamur.ai/free/en/papers/mialon-2023-augmented-language-models-survey/)
316. [**Orca: Progressive Learning from Complex Explanation Traces of GPT-4 arXiv:2306.02707**Subhabrata Mukherjee, Arindam Mitra, Ganesh Jawahar, et al.*2023 · 369 citations*](https://md.research.elamur.ai/free/en/papers/mukherjee-2023-orca-progressive-learning-complex-explanation/)
317. **[The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics](https://md.research.elamur.ai/free/en/papers/gehrmann-2021-gem-benchmark-natural-language-generation/) [Workshop on Generation, Evaluation and Metrics (GEM)](https://md.research.elamur.ai/free/en/venues/gem/)**Sebastian Gehrmann, Tosin Adewumi, Karmanya Aggarwal, et al.*2021 · 369 citations*
318. [**Revisiting Few-sample BERT Fine-tuning arXiv:2006.05987**Tianyi Zhang, Felix Wu, Arzoo Katiyar, et al.*2020 · 368 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2020-revisiting-few-sample-bert-fine/)
319. **[A Survey on Large Language Models for Code Generation](https://md.research.elamur.ai/free/en/papers/jiang-2024-survey-large-language-models-code/) [ACM Transactions on Software Engineering and Methodology](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-software-engineering-and-methodology/)**Juyong Jiang, Fan Wang, Jiasi Shen, et al.*2024 · 367 citations*
320. **[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 · 366 citations*
321. **[Pre-training is a Hot Topic: Contextualized Document Embeddings Improve Topic Coherence](https://md.research.elamur.ai/free/en/papers/bianchi-2021-pre-training-is-hot-topic/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Federico Bianchi, Silvia Terragni, Dirk Hovy*2021 · 366 citations*
322. **[The “Problem” of Human Label Variation: On Ground Truth in Data, Modeling and Evaluation](https://md.research.elamur.ai/free/en/papers/plank-2022-problem-human-label-variation-ground/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Barbara Plank*2022 · 364 citations*
323. [**Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback arXiv:2302.12813**Baolin Peng, Michel Galley, Pengcheng He, et al.*2023 · 363 citations*](https://md.research.elamur.ai/free/en/papers/peng-2023-check-facts-try-again-improving/)
324. **[HotFlip: White-Box Adversarial Examples for Text Classification](https://md.research.elamur.ai/free/en/papers/ebrahimi-2018-hotflip-white-box-adversarial-examples/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Javid Ebrahimi, Anyi Rao, Daniel Lowd, et al.*2018 · 363 citations*
325. **[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 · 362 citations*
326. **[Knowledgeable Prompt-tuning: Incorporating Knowledge into Prompt Verbalizer for Text Classification](https://md.research.elamur.ai/free/en/papers/hu-2022-knowledgeable-prompt-tuning-incorporating-knowledge/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shengding Hu, Ning Ding, Huadong Wang, et al.*2022 · 362 citations*
327. **[RARR: Researching and Revising What Language Models Say, Using Language Models](https://md.research.elamur.ai/free/en/papers/gao-2023-rarr-researching-revising-what-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Luyu Gao, Zhuyun Dai, Panupong Pasupat, et al.*2023 · 359 citations*
328. **[Varieties of conventional implicature](https://md.research.elamur.ai/free/en/papers/mccready-2010-varieties-conventional-implicature/) [Semantics and Pragmatics](https://md.research.elamur.ai/free/en/venues/semantics-and-pragmatics/)**Elin McCready*2010 · 359 citations*
329. [**Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference arXiv:2412.13663**Benjamin Warner, Antoine Chaffin, Benjamin Clavié, et al.*2024 · 358 citations*](https://md.research.elamur.ai/free/en/papers/warner-2024-smarter-better-faster-longer-modern/)
330. [**Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To! arXiv:2310.03693**Xiangyu Qi, Yi Zeng, Tinghao Xie, et al.*2023 · 355 citations*](https://md.research.elamur.ai/free/en/papers/qi-2023-fine-tuning-aligned-language-models/)
331. [**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/)
332. **[Lessons from Natural Language Inference in the Clinical Domain](https://md.research.elamur.ai/free/en/papers/romanov-2018-lessons-natural-language-inference-clinical/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Alexey Romanov, Chaitanya Shivade*2018 · 350 citations*
333. [**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/)
334. **[Representation Learning for Text-level Discourse Parsing](https://md.research.elamur.ai/free/en/papers/ji-2014-representation-learning-text-level-discourse/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yangfeng Ji, Jacob Eisenstein*2014 · 346 citations*
335. **[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 · 342 citations*
336. [**WARP: Word-level Adversarial ReProgramming arXiv:2101.00121**Karen Hambardzumyan, Hrant Khachatrian, Jonathan May*2021 · 340 citations*](https://md.research.elamur.ai/free/en/papers/hambardzumyan-2021-warp-word-level-adversarial-reprogramming/)
337. **[Word-level Textual Adversarial Attacking as Combinatorial Optimization](https://md.research.elamur.ai/free/en/papers/zang-2020-word-level-textual-adversarial-attacking/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yuan Zang, Fanchao Qi, Chenghao Yang, et al.*2020 · 340 citations*
338. **[Certified Robustness to Adversarial Word Substitutions](https://md.research.elamur.ai/free/en/papers/jia-2019-certified-robustness-adversarial-word-substitutions/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Robin Jia, Aditi Raghunathan, Kerem Göksel, et al.*2019 · 340 citations*
339. **[Large Language Models for Information Retrieval: A Survey](https://md.research.elamur.ai/free/en/papers/zhu-2023-large-language-models-information-retrieval/) [ACM Transactions on Information Systems](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-information-systems/)**Yutao Zhu; Huaying Yuan; Shuting Wang; Jiongnan Liu; Wenhan Liu; Chenlong Deng; Haonan Chen; Zheng Liu; Zhicheng Dou;, Ji-Rong Wen*2023 · 339 citations*
340. **[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 · 338 citations*
341. **[SKEP: Sentiment Knowledge Enhanced Pre-training for Sentiment Analysis](https://md.research.elamur.ai/free/en/papers/tian-2020-skep-sentiment-knowledge-enhanced-pre/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Hao Tian, Can Gao, Xinyan Xiao, et al.*2020 · 333 citations*
342. [**Can large language models provide useful feedback on research papers? A large-scale empirical analysis arXiv:2310.01783**Weixin Liang, Yuhui Zhang, Hancheng Cao, et al.*2023 · 332 citations*](https://md.research.elamur.ai/free/en/papers/liang-2023-large-language-models-provide/)
343. [**ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation arXiv:2107.02137**Yu Sun, Shuohuan Wang, Shikun Feng, et al.*2021 · 332 citations*](https://md.research.elamur.ai/free/en/papers/sun-2021-ernie-3-0-large/)
344. **[Language Is Not All You Need: Aligning Perception with Language Models](https://md.research.elamur.ai/free/en/papers/huang-2023-language-all-need-aligning-perception/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Shaohan Huang, Li Dong, Wenhui Wang, et al.*2023 · 330 citations*
345. [**Discovering Language Model Behaviors with Model-Written Evaluations arXiv:2212.09251**Ethan Perez, Sam Ringer, Kamilė Lukošiūtė, et al.*2022 · 328 citations*](https://md.research.elamur.ai/free/en/papers/perez-2022-discovering-language-model-behaviors-model/)
346. **[How Transferable are Neural Networks in NLP Applications?](https://md.research.elamur.ai/free/en/papers/mou-2016-how-transferable-are-neural-networks/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Lili Mou, Zhao Meng, Rui Yan, et al.*2016 · 328 citations*
347. **[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 · 327 citations*
348. [**CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing arXiv:2305.11738**Zhibin Gou, Zhihong Shao, Yeyun Gong, et al.*2023 · 326 citations*](https://md.research.elamur.ai/free/en/papers/gou-2023-critic-large-language-models-self/)
349. **[TIES-Merging: Resolving Interference When Merging Models](https://md.research.elamur.ai/free/en/papers/yadav-2023-ties-merging-resolving-interference-merging/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Prateek Yadav, Derek Tam, Leshem Choshen, et al.*2023 · 324 citations*
350. [**Text and Code Embeddings by Contrastive Pre-Training arXiv:2201.10005**Arvind Neelakantan, Tao Xu, Raul Puri, et al.*2022 · 323 citations*](https://md.research.elamur.ai/free/en/papers/neelakantan-2022-text-code-embeddings-contrastive-pre/)
351. **[The Fact Extraction and VERification (FEVER) Shared Task](https://md.research.elamur.ai/free/en/papers/thorne-2018-fact-extraction-verification-fever-shared/) [Fact Extraction and VERification Workshop (FEVER)](https://md.research.elamur.ai/free/en/venues/fever/)**James Thorne, Andreas Vlachos, Oana Cocarascu, et al.*2018 · 322 citations*
352. **[Distant Supervision for Relation Extraction beyond the Sentence Boundary](https://md.research.elamur.ai/free/en/papers/quirk-2017-distant-supervision-relation-extraction-beyond/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Chris Quirk, Hoifung Poon*2017 · 321 citations*
353. **[Paraphrasing Revisited with Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/mallinson-2017-paraphrasing-revisited-neural-machine-translation/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Jonathan Mallinson, Rico Sennrich, Mirella Lapata*2017 · 321 citations*
354. **[Towards Multimodal Sarcasm Detection (An _Obviously_ Perfect Paper)](https://md.research.elamur.ai/free/en/papers/castro-2019-towards-multimodal-sarcasm-detection-obviously/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Santiago Castro, Devamanyu Hazarika, Verónica Pérez-Rosas, et al.*2019 · 320 citations*
355. **[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 · 317 citations*
356. [**WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation arXiv:2201.05955**Alisa Liu, Swabha Swayamdipta, Noah A. Smith, et al.*2022 · 317 citations*](https://md.research.elamur.ai/free/en/papers/liu-2022-wanli-worker-ai-collaboration-natural/)
357. [**MEDITRON-70B: Scaling Medical Pretraining for Large Language Models arXiv:2311.16079**Zeming Chen, Alejandro Hernandez Cano, Angelika Romanou, et al.*2023 · 315 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-meditron-70b-scaling-medical-pretraining/)
358. **[Neural Natural Language Inference Models Enhanced with External Knowledge](https://md.research.elamur.ai/free/en/papers/chen-2018-neural-natural-language-inference-models/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Qian Chen, Xiaodan Zhu, Zhen-Hua Ling, et al.*2018 · 315 citations*
359. **[Explainable Automated Fact-Checking for Public Health Claims](https://md.research.elamur.ai/free/en/papers/kotonya-2020-explainable-automated-fact-checking-public/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Neema Kotonya, Francesca Toni*2020 · 314 citations*
360. **[Good-Enough Compositional Data Augmentation](https://md.research.elamur.ai/free/en/papers/andreas-2020-good-enough-compositional-data-augmentation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jacob Andreas*2020 · 313 citations*
361. [**Social Simulacra: Creating Populated Prototypes for Social Computing Systems arXiv:2208.04024**Joon Sung Park, Lindsay Popowski, Carrie J. Cai, et al.*2022 · 310 citations*](https://md.research.elamur.ai/free/en/papers/park-2022-social-simulacra-creating-populated-prototypes/)
362. [**Abductive Commonsense Reasoning**Chandra Bhagavatula, Ronan Le Bras, Chaitanya Malaviya, et al.*2019 · 309 citations*](https://md.research.elamur.ai/free/en/papers/bhagavatula-2019-abductive-commonsense-reasoning/)
363. **[UNIMO: Towards Unified-Modal Understanding and Generation via Cross-Modal Contrastive Learning](https://md.research.elamur.ai/free/en/papers/li-2021-unimo-unified-modal-understanding-generation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Wei Li, Can Gao, Guocheng Niu, et al.*2021 · 308 citations*
364. **[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*
365. **[SPoT: Better Frozen Model Adaptation through Soft Prompt Transfer](https://md.research.elamur.ai/free/en/papers/vu-2022-spot-better-frozen-model-adaptation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Tu Vu, Brian Lester, Noah Constant, et al.*2022 · 306 citations*
366. [**PromptRobust: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts arXiv:2306.04528**Kaijie Zhu, Jindong Wang, Jiaheng Zhou, et al.*2023 · 305 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2023-promptrobust-towards-evaluating-robustness-large/)
367. **[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 · 304 citations*
368. **[ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers](https://md.research.elamur.ai/free/en/papers/yao-2022-zeroquant-efficient-affordable-post-training/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Zhewei Yao, Reza Yazdani Aminabadi, Minjia Zhang, et al.*2022 · 304 citations*
369. **[SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimization](https://md.research.elamur.ai/free/en/papers/jiang-2020-smart-robust-efficient-fine-tuning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Haoming Jiang, Pengcheng He, Weizhu Chen, et al.*2020 · 304 citations*
370. **[Do Multi-Sense Embeddings Improve Natural Language Understanding?](https://md.research.elamur.ai/free/en/papers/li-2015-do-multi-sense-embeddings-improve/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jiwei Li, Dan Jurafsky*2015 · 304 citations*
371. **[Character-LLM: A Trainable Agent for Role-Playing](https://md.research.elamur.ai/free/en/papers/shao-2023-character-llm-trainable-agent-role/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yunfan Shao, Linyang Li, Junqi Dai, et al.*2023 · 303 citations*
372. **[Learning to Write with Cooperative Discriminators](https://md.research.elamur.ai/free/en/papers/holtzman-2018-learning-write-cooperative-discriminators/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ari Holtzman, Jan Buys, Maxwell Forbes, et al.*2018 · 303 citations*
373. **[Which argument is more convincing? Analyzing and predicting convincingness of Web arguments using bidirectional LSTM](https://md.research.elamur.ai/free/en/papers/habernal-2016-which-argument-is-more-convincing/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ivan Habernal, Iryna Gurevych*2016 · 303 citations*
374. [**Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language arXiv:2204.00598**Andy Zeng, Maria Attarian, Brian Ichter, et al.*2022 · 302 citations*](https://md.research.elamur.ai/free/en/papers/zeng-2022-socratic-models-composing-zero-shot/)
375. [**Retrieval-Augmented Generation for AI-Generated Content: A Survey arXiv:2402.19473**Penghao Zhao, Hailin Zhang, Qinhan Yu, et al.*2024 · 301 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2024-retrieval-augmented-generation-ai-generated/)
376. [**Challenges and Applications of Large Language Models arXiv:2307.10169**Jean Kaddour, Joshua Harris, Maximilian Mozes, et al.*2023 · 301 citations*](https://md.research.elamur.ai/free/en/papers/kaddour-2023-challenges-applications-large-language-models/)
377. **[Style Transformer: Unpaired Text Style Transfer without Disentangled Latent Representation](https://md.research.elamur.ai/free/en/papers/dai-2019-style-transformer-unpaired-text-style/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ning Dai, Jianze Liang, Xipeng Qiu, et al.*2019 · 301 citations*
378. **[Towards Debiasing Fact Verification Models](https://md.research.elamur.ai/free/en/papers/schuster-2019-debiasing-fact-verification-models/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Tal Schuster, Darsh Shah, Yun Jie Serene Yeo, et al.*2019 · 301 citations*
379. **[Want To Reduce Labeling Cost? GPT-3 Can Help](https://md.research.elamur.ai/free/en/papers/wang-2021-want-reduce-labeling-cost-gpt/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Shuohang Wang, Yang Liu, Yichong Xu, et al.*2021 · 300 citations*
380. **[Masked Language Modeling and the Distributional Hypothesis: Order Word Matters Pre-training for Little](https://md.research.elamur.ai/free/en/papers/sinha-2021-masked-language-modeling-distributional-hypothesis/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Koustuv Sinha, Robin Jia, Dieuwke Hupkes, et al.*2021 · 297 citations*
381. **[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*
382. **[BanglaBERT: Language Model Pretraining and Benchmarks for Low-Resource Language Understanding Evaluation in Bangla](https://md.research.elamur.ai/free/en/papers/bhattacharjee-2022-banglabert-language-model-pretraining-benchmarks/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Abhik Bhattacharjee, Tahmid Hasan, Wasi Ahmad, et al.*2022 · 296 citations*
383. [**Easy Victories and Uphill Battles in Coreference Resolution**Greg Durrett, Dan Klein*2013 · 293 citations*](https://md.research.elamur.ai/free/en/papers/durrett-2013-easy-victories-uphill-battles-coreference/)
384. **[Text Classification via Large Language Models](https://md.research.elamur.ai/free/en/papers/sun-2023-text-classification-large-language-models/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xiaofei Sun, Xiaoya Li, Jiwei Li, et al.*2023 · 292 citations*
385. **[Challenges and Strategies in Cross-Cultural NLP](https://md.research.elamur.ai/free/en/papers/hershcovich-2022-challenges-strategies-cross-cultural-nlp/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Daniel Hershcovich, Stella Frank, Heather Lent, et al.*2022 · 292 citations*
386. **[Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence](https://md.research.elamur.ai/free/en/papers/schuster-2021-get-your-vitamin-c-robust/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Tal Schuster, Adam Fisch, Regina Barzilay*2021 · 290 citations*
387. **[GEAR: Graph-based Evidence Aggregating and Reasoning for Fact Verification](https://md.research.elamur.ai/free/en/papers/zhou-2019-gear-graph-based-evidence-aggregating/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jie Zhou, Xu Han, Cheng Yang, et al.*2019 · 289 citations*
388. **[Computational Sociolinguistics: A Survey](https://md.research.elamur.ai/free/en/papers/nguyen-2016-computational-sociolinguistics-survey/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Dong Nguyen, A. Seza Doğruöz, Carolyn P. Rosé, et al.*2016 · 288 citations*
389. [**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/)
390. [**Cloze-driven Pretraining of Self-attention Networks arXiv:1903.07785**Alexei Baevski, Sergey Edunov, Yinhan Liu, et al.*2019 · 286 citations*](https://md.research.elamur.ai/free/en/papers/baevski-2019-cloze-driven-pretraining-self-attention/)
391. **[Metaphor as a Medium for Emotion: An Empirical Study](https://md.research.elamur.ai/free/en/papers/mohammad-2016-metaphor-as-medium-emotion-empirical/) [Joint Conference on Lexical and Computational Semantics (*SEM)](https://md.research.elamur.ai/free/en/venues/starsem/)**Saif Mohammad, Ekaterina Shutova, Peter Turney*2016 · 286 citations*
392. [**DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines arXiv:2310.03714**Omar Khattab, Arnav Singhvi, Paridhi Maheshwari, et al.*2023 · 285 citations*](https://md.research.elamur.ai/free/en/papers/khattab-2023-dspy-compiling-declarative-language-model/)
393. [**Larger language models do in-context learning differently arXiv:2303.03846**Jerry Wei, Jason Wei, Yi Tay, et al.*2023 · 285 citations*](https://md.research.elamur.ai/free/en/papers/wei-2023-larger-language-models-context-learning/)
394. [**MEGA: Multilingual Evaluation of Generative AI arXiv:2303.12528**Kabir Ahuja, Harshita Diddee, Rishav Hada, et al.*2023 · 285 citations*](https://md.research.elamur.ai/free/en/papers/ahuja-2023-mega-multilingual-evaluation-generative-ai/)
395. **[BAM! Born-Again Multi-Task Networks for Natural Language Understanding](https://md.research.elamur.ai/free/en/papers/clark-2019-bam-born-again-multi-task/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Kevin Clark, Minh-Thang Luong, Urvashi Khandelwal, et al.*2019 · 285 citations*
396. **[AlignScore: Evaluating Factual Consistency with A Unified Alignment Function](https://md.research.elamur.ai/free/en/papers/zha-2023-alignscore-evaluating-factual-consistency-unified/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yuheng Zha, Yichi Yang, Ruichen Li, et al.*2023 · 284 citations*
397. [**Scoring Coreference Partitions of Predicted Mentions: A Reference Implementation**Sameer Pradhan, Xiaoqiang Luo, Marta Recasens, et al.*2014 · 284 citations*](https://md.research.elamur.ai/free/en/papers/pradhan-2014-scoring-coreference-partitions-predicted-mentions/)
398. [**A Comprehensive Capability Analysis of GPT-3 and GPT-3.5 Series Models arXiv:2303.10420**Junjie Ye, Xuanting Chen, Nuo Xu, et al.*2023 · 283 citations*](https://md.research.elamur.ai/free/en/papers/ye-2023-comprehensive-capability-analysis-gpt-3/)
399. [**Ragas: Automated Evaluation of Retrieval Augmented Generation arXiv:2309.15217**Shahul Es, Jithin James, Luis Espinosa-Anke, et al.*2023 · 283 citations*](https://md.research.elamur.ai/free/en/papers/es-2023-ragas-automated-evaluation-retrieval-augmented/)
400. **[Contextualized Perturbation for Textual Adversarial Attack](https://md.research.elamur.ai/free/en/papers/li-2021-contextualized-perturbation-textual-adversarial-attack/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Dianqi Li, Yizhe Zhang, Hao Peng, et al.*2021 · 283 citations*

[← prev](https://md.research.elamur.ai/free/en/authors/christopher-e-potts/cited-by/page/3/) [1](https://md.research.elamur.ai/free/en/authors/christopher-e-potts/cited-by/) [2](https://md.research.elamur.ai/free/en/authors/christopher-e-potts/cited-by/page/2/) [3](https://md.research.elamur.ai/free/en/authors/christopher-e-potts/cited-by/page/3/) **4** [5](https://md.research.elamur.ai/free/en/authors/christopher-e-potts/cited-by/page/5/) … [10](https://md.research.elamur.ai/free/en/authors/christopher-e-potts/cited-by/page/10/) … [19](https://md.research.elamur.ai/free/en/authors/christopher-e-potts/cited-by/page/19/) [next →](https://md.research.elamur.ai/free/en/authors/christopher-e-potts/cited-by/page/5/)

- [← back to](https://md.research.elamur.ai/free/en/authors/christopher-e-potts/) — Christopher E. Potts