---
title: "Who cites Christopher E. Potts — page 5 — 9,352 citing papers"
description: "9,352 papers of the Elamur Research library cite the works of Christopher E. Among them: Best-Worst Scaling More Reliable than Rating…"
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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 5 of 19 · 401–500 · [← Christopher E. Potts](https://md.research.elamur.ai/free/en/authors/christopher-e-potts/)

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401. **[Best-Worst Scaling More Reliable than Rating Scales: A Case Study on Sentiment Intensity Annotation](https://md.research.elamur.ai/free/en/papers/kiritchenko-2017-best-worst-scaling-more-reliable/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Svetlana Kiritchenko, Saif Mohammad*2017 · 283 citations*
402. [**Generate rather than Retrieve: Large Language Models are Strong Context Generators arXiv:2209.10063**Wenhao Yu, Dan Iter, Shuohang Wang, et al.*2022 · 281 citations*](https://md.research.elamur.ai/free/en/papers/yu-2022-generate-rather-than-retrieve-large/)
403. **[Intermediate-Task Transfer Learning with Pretrained Language Models: When and Why Does It Work?](https://md.research.elamur.ai/free/en/papers/pruksachatkun-2020-intermediate-task-transfer-learning-pretrained/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yada Pruksachatkun, Jason Phang, Haokun Liu, et al.*2020 · 281 citations*
404. **[Compositional Vector Space Models for Knowledge Base Completion](https://md.research.elamur.ai/free/en/papers/neelakantan-2015-compositional-vector-space-models-knowledge/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Arvind Neelakantan, Benjamin Roth, Andrew McCallum*2015 · 281 citations*
405. **[ONION: A Simple and Effective Defense Against Textual Backdoor Attacks](https://md.research.elamur.ai/free/en/papers/qi-2021-onion-simple-effective-defense-against/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Fanchao Qi, Yangyi Chen, Mukai Li, et al.*2021 · 280 citations*
406. **[OpenPrompt: An Open-source Framework for Prompt-learning](https://md.research.elamur.ai/free/en/papers/ding-2022-openprompt-open-source-framework-prompt/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ning Ding, Shengding Hu, Weilin Zhao, et al.*2022 · 279 citations*
407. **[TRUE: Re-evaluating Factual Consistency Evaluation](https://md.research.elamur.ai/free/en/papers/honovich-2022-true-re-evaluating-factual-consistency/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Or Honovich, Roee Aharoni, Jonathan Herzig, et al.*2022 · 279 citations*
408. **[Polyjuice: Generating Counterfactuals for Explaining, Evaluating, and Improving Models](https://md.research.elamur.ai/free/en/papers/wu-2021-polyjuice-generating-counterfactuals-explaining-evaluating/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Tongshuang Wu, Marco Tulio Ribeiro, Jeffrey Heer, et al.*2021 · 277 citations*
409. **[Surface Form Competition: Why the Highest Probability Answer Isn't Always Right](https://md.research.elamur.ai/free/en/papers/holtzman-2021-surface-form-competition-why-highest/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Ari Holtzman, Peter West, Vered Shwartz, et al.*2021 · 277 citations*
410. **[Augmented SBERT: Data Augmentation Method for Improving Bi-Encoders for Pairwise Sentence Scoring Tasks](https://md.research.elamur.ai/free/en/papers/thakur-2021-augmented-sbert-data-augmentation-method/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Nandan Thakur, Nils Reimers, Johannes Daxenberger, et al.*2021 · 276 citations*
411. **[Text Classification Using Label Names Only: A Language Model Self-Training Approach](https://md.research.elamur.ai/free/en/papers/meng-2020-text-classification-label-names-only/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yu Meng, Yunyi Zhang, Jiaxin Huang, et al.*2020 · 274 citations*
412. **[Zero-Shot Transfer Learning for Event Extraction](https://md.research.elamur.ai/free/en/papers/huang-2018-zero-shot-transfer-learning-event/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Lifu Huang, Heng Ji, Kyunghyun Cho, et al.*2018 · 273 citations*
413. **[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 · 272 citations*
414. [**Hallucination-Free? Assessing the Reliability of Leading AI Legal Research Tools arXiv:2405.20362**Varun Magesh, Faiz Surani, Matthew Dahl, et al.*2024 · 271 citations*](https://md.research.elamur.ai/free/en/papers/magesh-2024-hallucination-free-assessing-reliability-leading/)
415. [**Towards Understanding and Mitigating Social Biases in Language Models arXiv:2106.13219**Paul Pu Liang, Chiyu Wu, Louis-Philippe Morency, et al.*2021 · 271 citations*](https://md.research.elamur.ai/free/en/papers/liang-2021-towards-understanding-mitigating-social-biases/)
416. **[COVIDLies: Detecting COVID-19 Misinformation on Social Media](https://md.research.elamur.ai/free/en/papers/hossain-2020-covidlies-detecting-covid-19-misinformation/) [Workshop on NLP for COVID-19 at ACL](https://md.research.elamur.ai/free/en/venues/nlpcovid19/)**Tamanna Hossain, Robert L. Logan IV, Arjuna Ugarte, et al.*2020 · 271 citations*
417. [**Towards Measuring the Representation of Subjective Global Opinions in Language Models arXiv:2306.16388**Esin Durmus, Karina Nguyen, Thomas I. Liao, et al.*2023 · 270 citations*](https://md.research.elamur.ai/free/en/papers/durmus-2023-towards-measuring-representation-subjective-global/)
418. **[GPT3Mix: Leveraging Large-scale Language Models for Text Augmentation](https://md.research.elamur.ai/free/en/papers/yoo-2021-gpt3mix-leveraging-large-scale-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Kang Min Yoo, Dongju Park, Jaewook Kang, et al.*2021 · 270 citations*
419. **[DiffCSE: Difference-based Contrastive Learning for Sentence Embeddings](https://md.research.elamur.ai/free/en/papers/chuang-2022-diffcse-difference-based-contrastive-learning/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Yung-Sung Chuang, Rumen Dangovski, Hongyin Luo, et al.*2022 · 269 citations*
420. **[HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Models](https://md.research.elamur.ai/free/en/papers/gutierrez-2024-hipporag-neurobiologically-inspired-long-term/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Bernal Jimenez Gutierrez, Yiheng Shu, Yu Gu, et al.*2024 · 268 citations*
421. **[Predictability and Surprise in Large Generative Models](https://md.research.elamur.ai/free/en/papers/ganguli-2022-predictability-surprise-large-generative-models/) [ACM Conference on Fairness, Accountability, and Transparency (FAccT)](https://md.research.elamur.ai/free/en/venues/acm-conference-on-fairness-accountability-and-transparency-facct/)**Deep Ganguli, Danny Hernandez, Liane Lovitt, et al.*2022 · 268 citations*
422. **[When Are Tree Structures Necessary for Deep Learning of Representations?](https://md.research.elamur.ai/free/en/papers/li-2015-when-are-tree-structures-necessary/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jiwei Li, Thang Luong, Dan Jurafsky, et al.*2015 · 267 citations*
423. **[An Analysis of Simple Data Augmentation for Named Entity Recognition](https://md.research.elamur.ai/free/en/papers/dai-2020-analysis-simple-data-augmentation-named/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Xiang Dai, Heike Adel*2020 · 266 citations*
424. **[Summarizing Opinions: Aspect Extraction Meets Sentiment Prediction and They Are Both Weakly Supervised](https://md.research.elamur.ai/free/en/papers/angelidis-2018-summarizing-opinions-aspect-extraction-meets/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Stefanos Angelidis, Mirella Lapata*2018 · 266 citations*
425. **[A Holistic Approach to Undesired Content Detection in the Real World](https://md.research.elamur.ai/free/en/papers/markov-2022-holistic-approach-undesired-content-detection/) [Proceedings of the AAAI Conference on Artificial Intelligence](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-aaai-conference-on-artificial-intelligence/)**Todor Markov, Chong Zhang, Sandhini Agarwal, et al.*2022 · 263 citations*
426. **[How Can We Accelerate Progress Towards Human-like Linguistic Generalization?](https://md.research.elamur.ai/free/en/papers/linzen-2020-how-can-we-accelerate-progress/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Tal Linzen*2020 · 263 citations*
427. **[CODRA: A Novel Discriminative Framework for Rhetorical Analysis](https://md.research.elamur.ai/free/en/papers/joty-2015-codra-novel-discriminative-framework-rhetorical/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Shafiq Joty, Giuseppe Carenini, Raymond T. Ng*2015 · 263 citations*
428. **[Large Language Models for Data Annotation and Synthesis: A Survey](https://md.research.elamur.ai/free/en/papers/tan-2024-large-language-models-data-annotation/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Zhen Tan, Dawei Li, Song Wang, et al.*2024 · 262 citations*
429. **[Towards Debiasing Sentence Representations](https://md.research.elamur.ai/free/en/papers/liang-2020-debiasing-sentence-representations/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Paul Pu Liang, Irene Mengze Li, Emily Zheng, et al.*2020 · 262 citations*
430. **[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 · 261 citations*
431. **[UKP-Athene: Multi-Sentence Textual Entailment for Claim Verification](https://md.research.elamur.ai/free/en/papers/hanselowski-2018-ukp-athene-multi-sentence-textual/) [Fact Extraction and VERification Workshop (FEVER)](https://md.research.elamur.ai/free/en/venues/fever/)**Andreas Hanselowski, Hao Zhang, Zile Li, et al.*2018 · 261 citations*
432. **[Unlearn Dataset Bias in Natural Language Inference by Fitting the Residual](https://md.research.elamur.ai/free/en/papers/he-2019-unlearn-dataset-bias-natural-language/) [Workshop on Deep Learning Approaches for Low-Resource NLP](https://md.research.elamur.ai/free/en/venues/workshop-on-deep-learning-approaches-for-low-resource-nlp/)**He He, Sheng Zha, Haohan Wang*2019 · 260 citations*
433. **[A Sensitivity Analysis of (and Practitioners' Guide to) Convolutional Neural Networks for Sentence Classification](https://md.research.elamur.ai/free/en/papers/zhang-2017-sensitivity-analysis-practitioners-guide-convolutional/) [International Joint Conference on Natural Language Processing (IJCNLP)](https://md.research.elamur.ai/free/en/venues/ijcnlp/)**Ye Zhang, Byron Wallace*2017 · 260 citations*
434. **[DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models](https://md.research.elamur.ai/free/en/papers/wang-2023-decodingtrust-comprehensive-assessment-trustworthiness-gpt/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Boxin Wang, Weixin Chen, Hengzhi Pei, et al.*2023 · 259 citations*
435. [**LexGLUE: A Benchmark Dataset for Legal Language Understanding in English arXiv:2110.00976**Ilias Chalkidis; Abhik Jana; Dirk Hartung; Michael Bommarito; Ion Androutsopoulos; Daniel Martin Katz;, Nikolaos Aletras*2021 · 258 citations*](https://md.research.elamur.ai/free/en/papers/chalkidis-2021-lexglue-benchmark-dataset-legal-language/)
436. **[Parameter-efficient Multi-task Fine-tuning for Transformers via Shared Hypernetworks](https://md.research.elamur.ai/free/en/papers/mahabadi-2021-parameter-efficient-multi-task-fine/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Rabeeh Karimi Mahabadi, Sebastian Ruder, Mostafa Dehghani, et al.*2021 · 258 citations*
437. **[Why Can GPT Learn In-Context? Language Models Secretly Perform Gradient Descent as Meta-Optimizers](https://md.research.elamur.ai/free/en/papers/dai-2023-why-can-gpt-learn-context/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Damai Dai, Yutao Sun, Li Dong, et al.*2023 · 257 citations*
438. **[The Multilingual Amazon Reviews Corpus](https://md.research.elamur.ai/free/en/papers/keung-2020-multilingual-amazon-reviews-corpus/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Phillip Keung, Yichao Lu, György Szarvas, et al.*2020 · 257 citations*
439. **[Humanity's Last Exam](https://md.research.elamur.ai/free/en/papers/phan-2025-humanity-s-last-exam/) [Nature](https://md.research.elamur.ai/free/en/venues/nature/)**Long Phan, Alice Gatti, Ziwen Han, et al.*2025 · 256 citations*
440. [**The Curse of Recursion: Training on Generated Data Makes Models Forget arXiv:2305.17493**Ilia Shumailov, Zakhar Shumaylov, Yiren Zhao, et al.*2023 · 255 citations*](https://md.research.elamur.ai/free/en/papers/shumailov-2023-curse-recursion-training-generated-data/)
441. **[Conversations Gone Awry: Detecting Early Signs of Conversational Failure](https://md.research.elamur.ai/free/en/papers/zhang-2018-conversations-gone-awry-detecting-early/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Justine Zhang, Jonathan Chang, Cristian Danescu-Niculescu-Mizil, et al.*2018 · 255 citations*
442. **[Multi-Reward Reinforced Summarization with Saliency and Entailment](https://md.research.elamur.ai/free/en/papers/pasunuru-2018-multi-reward-reinforced-summarization-saliency/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Ramakanth Pasunuru, Mohit Bansal*2018 · 255 citations*
443. **[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 · 254 citations*
444. [**Aya Model: An Instruction Finetuned Open-Access Multilingual Language Model arXiv:2402.07827**Ahmet Üstün, Viraat Aryabumi, Zheng-Xin Yong, et al.*2024 · 253 citations*](https://md.research.elamur.ai/free/en/papers/ustun-2024-aya-model-instruction-finetuned-open/)
445. **[GPT-RE: In-context Learning for Relation Extraction using Large Language Models](https://md.research.elamur.ai/free/en/papers/wan-2023-gpt-re-context-learning-relation/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Zhen Wan, Fei Cheng, Zhuoyuan Mao, et al.*2023 · 253 citations*
446. **[Entity-Centric Coreference Resolution with Model Stacking](https://md.research.elamur.ai/free/en/papers/clark-2015-entity-centric-coreference-resolution-model/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Kevin Clark, Christopher D. Manning*2015 · 253 citations*
447. [**RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment arXiv:2304.06767**Hanze Dong, Wei Xiong, Deepanshu Goyal, et al.*2023 · 252 citations*](https://md.research.elamur.ai/free/en/papers/dong-2023-raft-reward-ranked-finetuning-generative/)
448. **[Tree of Attacks: Jailbreaking Black-Box LLMs Automatically](https://md.research.elamur.ai/free/en/papers/mehrotra-2023-tree-attacks-jailbreaking-black-box/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Anay Mehrotra, Manolis Zampetakis, Paul Kassianik, et al.*2023 · 251 citations*
449. **[A Linear-Time Bottom-Up Discourse Parser with Constraints and Post-Editing](https://md.research.elamur.ai/free/en/papers/feng-2014-linear-time-bottom-up-discourse/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Vanessa Wei Feng, Graeme Hirst*2014 · 251 citations*
450. **[NLP Evaluation in trouble: On the Need to Measure LLM Data Contamination for each Benchmark](https://md.research.elamur.ai/free/en/papers/sainz-2023-nlp-evaluation-trouble-need-measure/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Oscar Sainz, Jon Campos, Iker García-Ferrero, et al.*2023 · 250 citations*
451. [**Red teaming ChatGPT via Jailbreaking: Bias, Robustness, Reliability and Toxicity arXiv:2301.12867**Terry Yue Zhuo, Yujin Huang, Chunyang Chen, et al.*2023 · 249 citations*](https://md.research.elamur.ai/free/en/papers/zhuo-2023-red-teaming-chatgpt-jailbreaking-bias/)
452. [**Learning and Evaluating General Linguistic Intelligence arXiv:1901.11373**Dani Yogatama, Cyprien de Masson d'Autume, Jerome Connor, et al.*2019 · 248 citations*](https://md.research.elamur.ai/free/en/papers/yogatama-2019-learning-evaluating-general-linguistic-intelligence/)
453. **[ZeroGen: Efficient Zero-shot Learning via Dataset Generation](https://md.research.elamur.ai/free/en/papers/ye-2022-zerogen-efficient-zero-shot-learning/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jiacheng Ye, Jiahui Gao, Qintong Li, et al.*2022 · 245 citations*
454. [**OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization arXiv:2212.12017**Srinivasan Iyer, Xi Victoria Lin, Ramakanth Pasunuru, et al.*2022 · 244 citations*](https://md.research.elamur.ai/free/en/papers/iyer-2022-opt-iml-scaling-language-model/)
455. **[Noisy Channel Language Model Prompting for Few-Shot Text Classification](https://md.research.elamur.ai/free/en/papers/min-2021-noisy-channel-language-model-prompting/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Sewon Min, Mike Lewis, Hannaneh Hajishirzi, et al.*2021 · 243 citations*
456. **[Argumentation Mining in User-Generated Web Discourse](https://md.research.elamur.ai/free/en/papers/habernal-2017-argumentation-mining-user-generated-web/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Ivan Habernal, Iryna Gurevych*2017 · 243 citations*
457. **[Synthetic Data Generation with Large Language Models for Text Classification: Potential and Limitations](https://md.research.elamur.ai/free/en/papers/li-2023-synthetic-data-generation-large-language/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Zhuoyan Li, Hangxiao Zhu, Zhuoran Lu, et al.*2023 · 242 citations*
458. **[Charagram: Embedding Words and Sentences via Character n-grams](https://md.research.elamur.ai/free/en/papers/wieting-2016-charagram-embedding-words-sentences-character/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**John Wieting, Mohit Bansal, Kevin Gimpel, et al.*2016 · 242 citations*
459. [**FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance arXiv:2305.05176**Lingjiao Chen, Matei Zaharia, James Zou*2023 · 241 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-frugalgpt-use-large-language-models/)
460. [**BlenderBot 3: a deployed conversational agent that continually learns to responsibly engage arXiv:2208.03188**Kurt Shuster, Jing Xu, Mojtaba Komeili, et al.*2022 · 241 citations*](https://md.research.elamur.ai/free/en/papers/shuster-2022-blenderbot-3-deployed-conversational-agent/)
461. **[Visual Adversarial Examples Jailbreak Aligned Large Language Models](https://md.research.elamur.ai/free/en/papers/qi-2023-visual-adversarial-examples-jailbreak-aligned/) [Proceedings of the AAAI Conference on Artificial Intelligence](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-aaai-conference-on-artificial-intelligence/)**Xiangyu Qi, Kaixuan Huang, Ashwinee Panda, et al.*2023 · 240 citations*
462. **[BERT-of-Theseus: Compressing BERT by Progressive Module Replacing](https://md.research.elamur.ai/free/en/papers/xu-2020-bert-theseus-compressing-bert-progressive/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Canwen Xu, Wangchunshu Zhou, Tao Ge, et al.*2020 · 240 citations*
463. **[Curriculum Learning for Natural Language Understanding](https://md.research.elamur.ai/free/en/papers/xu-2020-curriculum-learning-natural-language-understanding/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Benfeng Xu, Licheng Zhang, Zhendong Mao, et al.*2020 · 240 citations*
464. [**Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection arXiv:2302.12173**Kai Greshake, Sahar Abdelnabi, Shailesh Mishra, et al.*2023 · 239 citations*](https://md.research.elamur.ai/free/en/papers/greshake-2023-ve-signed-up-compromising-real/)
465. **[LexGLUE: A Benchmark Dataset for Legal Language Understanding in English](https://md.research.elamur.ai/free/en/papers/chalkidis-2022-lexglue-benchmark-dataset-legal-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ilias Chalkidis, Abhik Jana, Dirk Hartung, et al.*2022 · 239 citations*
466. **[FUDGE: Controlled Text Generation With Future Discriminators](https://md.research.elamur.ai/free/en/papers/yang-2021-fudge-controlled-text-generation-future/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Kevin Yang, Dan Klein*2021 · 239 citations*
467. **[Hidden Killer: Invisible Textual Backdoor Attacks with Syntactic Trigger](https://md.research.elamur.ai/free/en/papers/qi-2021-hidden-killer-invisible-textual-backdoor/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Fanchao Qi, Mukai Li, Yangyi Chen, et al.*2021 · 238 citations*
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469. **[mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval](https://md.research.elamur.ai/free/en/papers/zhang-2024-mgte-generalized-long-context-text/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Xin Zhang, Yanzhao Zhang, Dingkun Long, et al.*2024 · 237 citations*
470. **[On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey](https://md.research.elamur.ai/free/en/papers/long-2024-llms-driven-synthetic-data-generation/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Lin Long, Rui Wang, Ruixuan Xiao, et al.*2024 · 237 citations*
471. [**TrustLLM: Trustworthiness in Large Language Models arXiv:2401.05561**Yue Huang, Lichao Sun, Haoran Wang, et al.*2024 · 237 citations*](https://md.research.elamur.ai/free/en/papers/huang-2024-trustllm-trustworthiness-large-language-models/)
472. **[Principle-Driven Self-Alignment of Language Models from Scratch with Minimal Human Supervision](https://md.research.elamur.ai/free/en/papers/sun-2023-principle-driven-self-alignment-language/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Zhiqing Sun, Yikang Shen, Qinhong Zhou, et al.*2023 · 236 citations*
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475. **[Evaluating Commonsense in Pre-trained Language Models](https://md.research.elamur.ai/free/en/papers/zhou-2019-evaluating-commonsense-pre-trained-language/) [Proceedings of the AAAI Conference on Artificial Intelligence](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-aaai-conference-on-artificial-intelligence/)**Xuhui Zhou, Yue Zhang, Leyang Cui, et al.*2019 · 235 citations*
476. **[Better Document-level Sentiment Analysis from RST Discourse Parsing](https://md.research.elamur.ai/free/en/papers/bhatia-2015-better-document-level-sentiment-analysis/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Parminder Bhatia, Yangfeng Ji, Jacob Eisenstein*2015 · 235 citations*
477. **[Self-Guided Contrastive Learning for BERT Sentence Representations](https://md.research.elamur.ai/free/en/papers/kim-2021-self-guided-contrastive-learning-bert/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Taeuk Kim, Kang Min Yoo, Sang-goo Lee*2021 · 234 citations*
478. **[Evaluating Open-Domain Question Answering in the Era of Large Language Models](https://md.research.elamur.ai/free/en/papers/kamalloo-2023-evaluating-open-domain-question-answering/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ehsan Kamalloo, Nouha Dziri, Charles Clarke, et al.*2023 · 233 citations*
479. [**Steering Language Models With Activation Engineering arXiv:2308.10248**Alexander Matt Turner, Lisa Thiergart, Gavin Leech, et al.*2023 · 233 citations*](https://md.research.elamur.ai/free/en/papers/turner-2023-steering-language-models-activation-engineering/)
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481. [**GAIA: a benchmark for General AI Assistants arXiv:2311.12983**Gregoire Mialon, Clementine Fourrier, Craig Swift, et al.*2023 · 232 citations*](https://md.research.elamur.ai/free/en/papers/mialon-2023-gaia-benchmark-general-ai-assistants/)
482. **[Beyond neural scaling laws: beating power law scaling via data pruning](https://md.research.elamur.ai/free/en/papers/sorscher-2022-neural-scaling-laws-beating-power/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Ben Sorscher, Robert Geirhos, Shashank Shekhar, et al.*2022 · 231 citations*
483. [**Self-critiquing models for assisting human evaluators arXiv:2206.05802**William Saunders, Catherine Yeh, Jeff Wu, et al.*2022 · 228 citations*](https://md.research.elamur.ai/free/en/papers/saunders-2022-self-critiquing-models-assisting-human/)
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485. **[HoVer: A Dataset for Many-Hop Fact Extraction And Claim Verification](https://md.research.elamur.ai/free/en/papers/jiang-2020-hover-dataset-many-hop-fact/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yichen Jiang, Shikha Bordia, Zheng Zhong, et al.*2020 · 228 citations*
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487. **[The Importance of Being Recurrent for Modeling Hierarchical Structure](https://md.research.elamur.ai/free/en/papers/tran-2018-importance-being-recurrent-modeling-hierarchical/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Ke Tran, Arianna Bisazza, Christof Monz*2018 · 228 citations*
488. **[Sparse Overcomplete Word Vector Representations](https://md.research.elamur.ai/free/en/papers/faruqui-2015-sparse-overcomplete-word-vector-representations/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Manaal Faruqui, Yulia Tsvetkov, Dani Yogatama, et al.*2015 · 228 citations*
489. [**A Survey of Hallucination in Large Foundation Models arXiv:2309.05922**Vipula Rawte, Amit Sheth, Amitava Das*2023 · 226 citations*](https://md.research.elamur.ai/free/en/papers/rawte-2023-survey-hallucination-large-foundation-models/)
490. **[Learning to Faithfully Rationalize by Construction](https://md.research.elamur.ai/free/en/papers/jain-2020-learning-faithfully-rationalize-construction/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Sarthak Jain, Sarah Wiegreffe, Yuval Pinter, et al.*2020 · 225 citations*
491. [**Reinforced Self-Training (ReST) for Language Modeling arXiv:2308.08998**Caglar Gulcehre, Tom Le Paine, Srivatsan Srinivasan, et al.*2023 · 224 citations*](https://md.research.elamur.ai/free/en/papers/gulcehre-2023-reinforced-self-training-rest-language/)
492. [**NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models arXiv:2405.17428**Chankyu Lee, Rajarshi Roy, Mengyao Xu, et al.*2024 · 222 citations*](https://md.research.elamur.ai/free/en/papers/lee-2024-nv-embed-improved-techniques-training/)
493. [**Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP arXiv:2212.14024**Omar Khattab, Keshav Santhanam, Xiang Lisa Li, et al.*2022 · 222 citations*](https://md.research.elamur.ai/free/en/papers/khattab-2022-demonstrate-search-predict-composing-retrieval/)
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495. **[Visualizing and Understanding the Effectiveness of BERT](https://md.research.elamur.ai/free/en/papers/hao-2019-visualizing-understanding-effectiveness-bert/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yaru Hao, Li Dong, Furu Wei, et al.*2019 · 222 citations*
496. [**OpenHands: An Open Platform for AI Software Developers as Generalist Agents arXiv:2407.16741**Xingyao Wang, Boxuan Li, Yufan Song, et al.*2024 · 221 citations*](https://md.research.elamur.ai/free/en/papers/wang-2024-openhands-open-platform-ai/)
497. **[Structured Pruning Learns Compact and Accurate Models](https://md.research.elamur.ai/free/en/papers/xia-2022-structured-pruning-learns-compact-accurate/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mengzhou Xia, Zexuan Zhong, Danqi Chen*2022 · 221 citations*
498. **[Measuring Association Between Labels and Free-Text Rationales](https://md.research.elamur.ai/free/en/papers/wiegreffe-2021-measuring-association-between-labels-free/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Sarah Wiegreffe, Ana Marasović, Noah A. Smith*2021 · 220 citations*
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500. **[Distilling Reasoning Capabilities into Smaller Language Models](https://md.research.elamur.ai/free/en/papers/shridhar-2023-distilling-reasoning-capabilities-into-smaller/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Kumar Shridhar, Alessandro Stolfo, Mrinmaya Sachan*2023 · 219 citations*

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