---
title: "Who cites Adversarial NLI: A New — 659 citing papers"
description: "659 papers of the Elamur Research library cite «Adversarial NLI: A New Benchmark for Natural Language Understanding», found in their…"
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# Who cites «Adversarial NLI: A New Benchmark for Natural Language Understanding»

659 papers of our library cite it (of the 149,439 whose bibliographies we read) · 152 with a page here, the most cited first · page 1 of 2 · 1–100 · [← back to «Adversarial NLI: A New Benchmark for Natural Language Understanding»](https://md.research.elamur.ai/free/en/papers/nie-2020-adversarial-nli-new-benchmark-natural/)

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1. [**Language Models are Few-Shot Learners arXiv:2005.14165**Tom B. Brown, Benjamin Mann, Nick Ryder, et al.*2020 · 14,178 citations*](https://md.research.elamur.ai/free/en/papers/brown-2020-language-models-few-shot-learners/)
2. [**PaLM: Scaling Language Modeling with Pathways arXiv:2204.02311**Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, et al.*2022 · 4,753 citations*](https://md.research.elamur.ai/free/en/papers/chowdhery-2022-palm-scaling-language-modeling-pathways/)
3. **[SimCSE: Simple Contrastive Learning of Sentence Embeddings](https://md.research.elamur.ai/free/en/papers/gao-2021-simcse-simple-contrastive-learning-sentence/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Tianyu Gao, Xingcheng Yao, Danqi Chen*2021 · 3,942 citations*
4. **[A Survey on Evaluation of Large Language Models](https://md.research.elamur.ai/free/en/papers/chang-2023-survey-evaluation-large-language-models/) [ACM Transactions on Intelligent Systems and Technology](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-intelligent-systems-and-technology/)**Yupeng Chang, Xu Wang, Jindong Wang, et al.*2023 · 3,648 citations*
5. [**Self-Consistency Improves Chain of Thought Reasoning in Language Models arXiv:2203.11171**Xuezhi Wang, Jason Wei, Dale Schuurmans, et al.*2022 · 3,121 citations*](https://md.research.elamur.ai/free/en/papers/wang-2022-self-consistency-improves-chain-thought/)
6. [**Finetuned Language Models Are Zero-Shot Learners arXiv:2109.01652**Jason Wei, Maarten Bosma, Vincent Y. Zhao, et al.*2021 · 1,954 citations*](https://md.research.elamur.ai/free/en/papers/wei-2021-finetuned-language-models-zero-shot/)
7. [**Multitask Prompted Training Enables Zero-Shot Task Generalization arXiv:2110.08207**Victor Sanh, Albert Webson, Colin Raffel, et al.*2021 · 1,612 citations*](https://md.research.elamur.ai/free/en/papers/sanh-2021-multitask-prompted-training-enables-zero/)
8. **[Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/min-2021-recent-advances-natural-language-processing/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Bonan Min, Hayley Ross, Elior Sulem, et al.*2021 · 1,556 citations*
9. **[A Comprehensive Overview of Large Language Models](https://md.research.elamur.ai/free/en/papers/naveed-2023-comprehensive-overview-large-language-models/) [ACM Transactions on Intelligent Systems and Technology](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-intelligent-systems-and-technology/)**Humza Naveed, Asad Ullah Khan, Shi Qiu, et al.*2023 · 1,287 citations*
10. [**Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone arXiv:2404.14219**Marah Abdin, Jyoti Aneja, Hany Awadalla, et al.*2024 · 1,134 citations*](https://md.research.elamur.ai/free/en/papers/abdin-2024-phi-3-technical-report-highly/)
11. [**PaLM 2 Technical Report arXiv:2305.10403**Rohan Anil, Andrew M. Dai, Orhan Firat, et al.*2023 · 1,000 citations*](https://md.research.elamur.ai/free/en/papers/anil-2023-palm-2-technical-report/)
12. [**Are NLP Models really able to Solve Simple Math Word Problems?**Arkil Patel, Satwik Bhattamishra, Navin Goyal*2021 · 890 citations*](https://md.research.elamur.ai/free/en/papers/patel-2021-are-nlp-models-really-able/)
13. **[GPT-NeoX-20B: An Open-Source Autoregressive Language Model](https://md.research.elamur.ai/free/en/papers/black-2022-gpt-neox-20b-open-source/) [BigScience Episode – Workshop on Challenges & Perspectives in Creating Large Language Models](https://md.research.elamur.ai/free/en/venues/bigscience/)**Sidney Black, Stella Biderman, Eric Hallahan, et al.*2022 · 831 citations*
14. **[Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond](https://md.research.elamur.ai/free/en/papers/yang-2023-harnessing-power-llms-practice-survey/) [ACM Transactions on Knowledge Discovery from Data](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-knowledge-discovery-from-data/)**Jingfeng Yang, Hongye Jin, Ruixiang Tang, et al.*2023 · 814 citations*
15. **[Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning](https://md.research.elamur.ai/free/en/papers/liu-2022-few-shot-parameter-efficient-fine/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Haokun Liu, Derek Tam, Mohammed Muqeeth, et al.*2022 · 754 citations*
16. **[Red Teaming Language Models with Language Models](https://md.research.elamur.ai/free/en/papers/perez-2022-red-teaming-language-models-language/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Ethan Perez, Saffron Huang, Francis Song, et al.*2022 · 700 citations*
17. **[Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes](https://md.research.elamur.ai/free/en/papers/hsieh-2023-distilling-step-step-outperforming-larger/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Cheng-Yu Hsieh, Chun-Liang Li, Chih-kuan Yeh, et al.*2023 · 690 citations*
18. [**BloombergGPT: A Large Language Model for Finance arXiv:2303.17564**Shijie Wu, Ozan Irsoy, Steven Lu, et al.*2023 · 618 citations*](https://md.research.elamur.ai/free/en/papers/wu-2023-bloomberggpt-large-language-model-finance/)
19. [**Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model arXiv:2201.11990**Shaden Smith, Mostofa Patwary, Brandon Norick, et al.*2022 · 615 citations*](https://md.research.elamur.ai/free/en/papers/smith-2022-using-deepspeed-megatron-train-megatron/)
20. [**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/)
21. **[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*
22. **[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*
23. [**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/)
24. [**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/)
25. **[oLMpics — On what Language Model Pre-training Captures](https://md.research.elamur.ai/free/en/papers/talmor-2019-olmpics-language-model-pre-training/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Alon Talmor, Yanai Elazar, Yoav Goldberg, et al.*2019 · 440 citations*
26. **[Do Prompt-Based Models Really Understand the Meaning of Their Prompts?](https://md.research.elamur.ai/free/en/papers/webson-2022-do-prompt-based-models-really/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Albert Webson, Ellie Pavlick*2022 · 407 citations*
27. **[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*
28. [**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/)
29. [**The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence arXiv:2002.06177**Gary Marcus*2020 · 332 citations*](https://md.research.elamur.ai/free/en/papers/marcus-2020-next-decade-ai-four/)
30. [**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/)
31. **[Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection](https://md.research.elamur.ai/free/en/papers/vidgen-2021-learning-worst-dynamically-generated-datasets/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Bertie Vidgen, Tristan Thrush, Zeerak Waseem, et al.*2021 · 325 citations*
32. **[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*
33. [**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/)
34. **[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*
35. **[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*
36. [**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/)
37. **[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*
38. **[QAFactEval: Improved QA-Based Factual Consistency Evaluation for Summarization](https://md.research.elamur.ai/free/en/papers/fabbri-2022-qafacteval-improved-qa-based-factual/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Alexander Fabbri, Chien-Sheng Wu, Wenhao Liu, et al.*2022 · 292 citations*
39. **[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*
40. **[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*
41. **[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*
42. **[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*
43. **[RedPajama: an Open Dataset for Training Large Language Models](https://md.research.elamur.ai/free/en/papers/weber-2024-redpajama-open-dataset-training-large/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Maurice Weber, Daniel Fu, Quentin Anthony, et al.*2024 · 260 citations*
44. **[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*
45. [**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/)
46. **[Syntactic Data Augmentation Increases Robustness to Inference Heuristics](https://md.research.elamur.ai/free/en/papers/min-2020-syntactic-data-augmentation-increases-robustness/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Junghyun Min, R. Thomas McCoy, Dipanjan Das, et al.*2020 · 220 citations*
47. **[“Kelly is a Warm Person, Joseph is a Role Model”: Gender Biases in LLM-Generated Reference Letters](https://md.research.elamur.ai/free/en/papers/wan-2023-kelly-is-warm-person-joseph/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yixin Wan, George Pu, Jiao Sun, et al.*2023 · 208 citations*
48. **[HateCheck: Functional Tests for Hate Speech Detection Models](https://md.research.elamur.ai/free/en/papers/rottger-2021-hatecheck-functional-tests-hate-speech/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Paul Röttger, Bertie Vidgen, Dong Nguyen, et al.*2021 · 204 citations*
49. [**Recipes for Safety in Open-domain Chatbots arXiv:2010.07079**Jing Xu, Da Ju, Margaret Li, et al.*2020 · 201 citations*](https://md.research.elamur.ai/free/en/papers/xu-2020-recipes-safety-open-domain-chatbots/)
50. **[A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark Datasets](https://md.research.elamur.ai/free/en/papers/laskar-2023-systematic-study-comprehensive-evaluation-chatgpt/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Md Tahmid Rahman Laskar, M Saiful Bari, Mizanur Rahman, et al.*2023 · 200 citations*
51. **[What Can We Learn from Collective Human Opinions on Natural Language Inference Data?](https://md.research.elamur.ai/free/en/papers/nie-2020-what-can-we-learn-collective/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yixin Nie, Xiang Zhou, Mohit Bansal*2020 · 196 citations*
52. [**Evaluating Large Language Models: A Comprehensive Survey arXiv:2310.19736**Zishan Guo, Renren Jin, Chuang Liu, et al.*2023 · 182 citations*](https://md.research.elamur.ai/free/en/papers/guo-2023-evaluating-large-language-models-comprehensive/)
53. **[Translation Artifacts in Cross-lingual Transfer Learning](https://md.research.elamur.ai/free/en/papers/artetxe-2020-translation-artifacts-cross-lingual-transfer/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Mikel Artetxe, Gorka Labaka, Eneko Agirre*2020 · 176 citations*
54. [**Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision arXiv:2312.09390**Collin Burns, Pavel Izmailov, Jan Hendrik Kirchner, et al.*2023 · 172 citations*](https://md.research.elamur.ai/free/en/papers/burns-2023-weak-strong-generalization-eliciting-strong/)
55. **[QuALITY: Question Answering with Long Input Texts, Yes!](https://md.research.elamur.ai/free/en/papers/pang-2022-quality-question-answering-long-input/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Richard Yuanzhe Pang, Alicia Parrish, Nitish Joshi, et al.*2022 · 167 citations*
56. **[MiniCheck: Efficient Fact-Checking of LLMs on Grounding Documents](https://md.research.elamur.ai/free/en/papers/tang-2024-minicheck-efficient-fact-checking-llms/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Liyan Tang, Philippe Laban, Greg Durrett*2024 · 166 citations*
57. [**Leveraging Large Language Models for Multiple Choice Question Answering arXiv:2210.12353**Joshua Robinson, Christopher Michael Rytting, David Wingate*2022 · 157 citations*](https://md.research.elamur.ai/free/en/papers/robinson-2022-leveraging-large-language-models-multiple/)
58. **[ReConcile: Round-Table Conference Improves Reasoning via Consensus among Diverse LLMs](https://md.research.elamur.ai/free/en/papers/chen-2024-reconcile-round-table-conference-improves/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Justin Chen, Swarnadeep Saha, Mohit Bansal*2024 · 156 citations*
59. **[INFOTABS: Inference on Tables as Semi-structured Data](https://md.research.elamur.ai/free/en/papers/gupta-2020-infotabs-inference-tables-as-semi/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Vivek Gupta, Maitrey Mehta, Pegah Nokhiz, et al.*2020 · 150 citations*
60. **[Measure and Improve Robustness in NLP Models: A Survey](https://md.research.elamur.ai/free/en/papers/wang-2022-measure-improve-robustness-nlp-models/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Xuezhi Wang, Haohan Wang, Diyi Yang*2022 · 148 citations*
61. **[Out of Order: How important is the sequential order of words in a sentence in Natural Language Understanding tasks?](https://md.research.elamur.ai/free/en/papers/pham-2021-out-order-how-important-is/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Thang Pham, Trung Bui, Long Mai, et al.*2021 · 148 citations*
62. [**OCNLI: Original Chinese Natural Language Inference arXiv:2010.05444**Hai Hu, Kyle Richardson, Liang Xu, et al.*2020 · 148 citations*](https://md.research.elamur.ai/free/en/papers/hu-2020-ocnli-original-chinese-natural/)
63. **[A Token-level Reference-free Hallucination Detection Benchmark for Free-form Text Generation](https://md.research.elamur.ai/free/en/papers/liu-2022-token-level-reference-free-hallucination/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Tianyu Liu, Yizhe Zhang, Chris Brockett, et al.*2022 · 142 citations*
64. **[Bot-Adversarial Dialogue for Safe Conversational Agents](https://md.research.elamur.ai/free/en/papers/xu-2021-bot-adversarial-dialogue-safe-conversational/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Jing Xu, Da Ju, Margaret Li, et al.*2021 · 141 citations*
65. **[UnNatural Language Inference](https://md.research.elamur.ai/free/en/papers/sinha-2021-unnatural-language-inference/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Koustuv Sinha, Prasanna Parthasarathi, Joelle Pineau, et al.*2021 · 135 citations*
66. **[What to Pre-Train on? Efficient Intermediate Task Selection](https://md.research.elamur.ai/free/en/papers/poth-2021-what-pre-train-efficient-intermediate/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Clifton Poth, Jonas Pfeiffer, Andreas Rücklé, et al.*2021 · 132 citations*
67. [**Neural Natural Language Inference Models Partially Embed Theories of Lexical Entailment and Negation**Atticus Geiger, Kyle Richardson, Christopher Potts*2020 · 123 citations*](https://md.research.elamur.ai/free/en/papers/geiger-2020-neural-natural-language-inference-models/)
68. **[DocNLI: A Large-scale Dataset for Document-level Natural Language Inference](https://md.research.elamur.ai/free/en/papers/yin-2021-docnli-large-scale-dataset-document/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Wenpeng Yin, Dragomir Radev, Caiming Xiong*2021 · 121 citations*
69. **[Testing the Ability of Language Models to Interpret Figurative Language](https://md.research.elamur.ai/free/en/papers/liu-2022-testing-ability-language-models-interpret/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Emmy Liu, Chenxuan Cui, Kenneth Zheng, et al.*2022 · 114 citations*
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