---
title: "Who cites Aparajita Haldar — 442 papers of our library"
description: "442 papers of the Elamur Research library cite the works of Aparajita Haldar, found in their bibliographies · page 1 of 2 · 1–100; the most cited first."
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---
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# Who cites Aparajita Haldar

468 citations of their works from 442 papers of our library (of the 149,439 whose bibliographies we read) · 132 with a page here, the most cited first · page 1 of 2 · 1–100 · [← Aparajita Haldar](https://md.research.elamur.ai/free/en/authors/aparajita-haldar/)

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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. **[Survey of Hallucination in Natural Language Generation](https://md.research.elamur.ai/free/en/papers/ji-2022-survey-hallucination-natural-language-generation/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Ziwei Ji, Nayeon Lee, Rita Frieske, et al.*2022 · 5,974 citations*
3. [**On the Opportunities and Risks of Foundation Models arXiv:2108.07258**Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, et al.*2021 · 3,160 citations*](https://md.research.elamur.ai/free/en/papers/bommasani-2021-opportunities-risks-foundation-models/)
4. **[QLoRA: Efficient Finetuning of Quantized LLMs](https://md.research.elamur.ai/free/en/papers/dettmers-2023-qlora-efficient-finetuning-quantized-llms/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, et al.*2023 · 2,929 citations*
5. [**SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems arXiv:1905.00537**Alex Wang, Yada Pruksachatkun, Nikita Nangia, et al.*2019 · 2,227 citations*](https://md.research.elamur.ai/free/en/papers/wang-2019-superglue-stickier-benchmark-general-purpose/)
6. **[BERT Rediscovers the Classical NLP Pipeline](https://md.research.elamur.ai/free/en/papers/tenney-2019-bert-rediscovers-classical-nlp-pipeline/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ian Tenney, Dipanjan Das, Ellie Pavlick*2019 · 2,028 citations*
7. **[CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge](https://md.research.elamur.ai/free/en/papers/talmor-2019-commonsenseqa-question-answering-challenge-targeting/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Alon Talmor, Jonathan Herzig, Nicholas Lourie, et al.*2019 · 1,676 citations*
8. [**Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference**R. Thomas McCoy, Ellie Pavlick, Tal Linzen*2019 · 1,662 citations*](https://md.research.elamur.ai/free/en/papers/mccoy-2019-right-wrong-reasons-diagnosing-syntactic/)
9. [**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/)
10. **[BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions](https://md.research.elamur.ai/free/en/papers/clark-2019-boolq-exploring-surprising-difficulty-natural/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Christopher Clark, Kenton Lee, Ming-Wei Chang, et al.*2019 · 1,330 citations*
11. **[Adversarial NLI: A New Benchmark for Natural Language Understanding](https://md.research.elamur.ai/free/en/papers/nie-2020-adversarial-nli-new-benchmark-natural/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yixin Nie, Adina Williams, Emily Dinan, et al.*2020 · 1,296 citations*
12. **[PIQA: Reasoning about Physical Commonsense in Natural Language](https://md.research.elamur.ai/free/en/papers/bisk-2019-piqa-reasoning-about-physical-commonsense/) [Proceedings of the AAAI Conference on Artificial Intelligence](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-aaai-conference-on-artificial-intelligence/)**Yonatan Bisk, Rowan Zellers, Ronan Le Bras, et al.*2019 · 1,264 citations*
13. [**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/)
14. **[Analysis Methods in Neural Language Processing: A Survey](https://md.research.elamur.ai/free/en/papers/belinkov-2019-analysis-methods-neural-language-processing/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Yonatan Belinkov, James Glass*2019 · 740 citations*
15. **[A Survey on Automated Fact-Checking](https://md.research.elamur.ai/free/en/papers/guo-2022-survey-automated-fact-checking/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Zhijiang Guo, Michael Schlichtkrull, Andreas Vlachos*2022 · 647 citations*
16. [**What do you learn from context? Probing for sentence structure in contextualized word representations arXiv:1905.06316**Ian Tenney, Patrick Xia, Berlin Chen, et al.*2019 · 602 citations*](https://md.research.elamur.ai/free/en/papers/tenney-2019-learn-context-probing-sentence-structure/)
17. **[Neural Text Summarization: A Critical Evaluation](https://md.research.elamur.ai/free/en/papers/kryscinski-2019-neural-text-summarization-critical-evaluation/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Wojciech Kryscinski, Nitish Shirish Keskar, Bryan McCann, et al.*2019 · 559 citations*
18. [**The Natural Language Decathlon: Multitask Learning as Question Answering arXiv:1806.08730**Bryan McCann, Nitish Shirish Keskar, Caiming Xiong, et al.*2018 · 545 citations*](https://md.research.elamur.ai/free/en/papers/mccann-2018-natural-language-decathlon-multitask-learning/)
19. [**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/)
20. [**Breaking NLI Systems with Sentences that Require Simple Lexical Inferences**Max Glockner, Vered Shwartz, Yoav Goldberg*2018 · 525 citations*](https://md.research.elamur.ai/free/en/papers/glockner-2018-breaking-nli-systems-sentences-that/)
21. [**Inherent Disagreements in Human Textual Inferences**Ellie Pavlick, Tom Kwiatkowski*2019 · 454 citations*](https://md.research.elamur.ai/free/en/papers/pavlick-2019-inherent-disagreements-human-textual-inferences/)
22. **[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*
23. [**Datasets: A Community Library for Natural Language Processing arXiv:2109.02846**Quentin Lhoest, Albert Villanova del Moral, Yacine Jernite, et al.*2021 · 368 citations*](https://md.research.elamur.ai/free/en/papers/lhoest-2021-datasets-community-library-natural-language/)
24. [**How Much Reading Does Reading Comprehension Require? A Critical Investigation of Popular Benchmarks**Divyansh Kaushik, Zachary C. Lipton*2018 · 364 citations*](https://md.research.elamur.ai/free/en/papers/kaushik-2018-how-much-reading-does-reading/)
25. [**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/)
26. [**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/)
27. **[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*
28. **[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*
29. **[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*
30. [**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/)
31. **[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*
32. **[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*
33. [**Measuring and Reducing Gendered Correlations in Pre-trained Models arXiv:2010.06032**Kellie Webster, Xuezhi Wang, Ian Tenney, et al.*2020 · 254 citations*](https://md.research.elamur.ai/free/en/papers/webster-2020-measuring-reducing-gendered-correlations-pre/)
34. [**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/)
35. **[What Happens To BERT Embeddings During Fine-tuning?](https://md.research.elamur.ai/free/en/papers/merchant-2020-what-happens-bert-embeddings-during/) [BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP](https://md.research.elamur.ai/free/en/venues/blackboxnlp/)**Amil Merchant, Elahe Rahimtoroghi, Ellie Pavlick, et al.*2020 · 220 citations*
36. **[Sources of Hallucination by Large Language Models on Inference Tasks](https://md.research.elamur.ai/free/en/papers/mckenna-2023-sources-hallucination-large-language-models/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Nick McKenna, Tianyi Li, Liang Cheng, et al.*2023 · 212 citations*
37. [**Prompting GPT-3 To Be Reliable arXiv:2210.09150**Chenglei Si, Zhe Gan, Zhengyuan Yang, et al.*2022 · 210 citations*](https://md.research.elamur.ai/free/en/papers/si-2022-prompting-gpt-3-reliable/)
38. **[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*
39. **[The Right Tool for the Job: Matching Model and Instance Complexities](https://md.research.elamur.ai/free/en/papers/schwartz-2020-right-tool-job-matching-model/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Roy Schwartz, Gabriel Stanovsky, Swabha Swayamdipta, et al.*2020 · 196 citations*
40. **[Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation](https://md.research.elamur.ai/free/en/papers/poliak-2018-collecting-diverse-natural-language-inference/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Adam Poliak, Aparajita Haldar, Rachel Rudinger, et al.*2018 · 193 citations*
41. **[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*
42. **[Impact of Pretraining Term Frequencies on Few-Shot Numerical Reasoning](https://md.research.elamur.ai/free/en/papers/razeghi-2022-impact-pretraining-term-frequencies-few/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yasaman Razeghi, Robert L Logan IV, Matt Gardner, et al.*2022 · 165 citations*
43. **[Ethics Sheet for Automatic Emotion Recognition and Sentiment Analysis](https://md.research.elamur.ai/free/en/papers/mohammad-2022-ethics-sheet-automatic-emotion-recognition/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Saif M. Mohammad*2022 · 164 citations*
44. **[Probing What Different NLP Tasks Teach Machines about Function Word Comprehension](https://md.research.elamur.ai/free/en/papers/kim-2019-probing-what-different-nlp-tasks/) [Joint Conference on Lexical and Computational Semantics (*SEM)](https://md.research.elamur.ai/free/en/venues/starsem/)**Najoung Kim, Roma Patel, Adam Poliak, et al.*2019 · 163 citations*
45. **[Question Answering for Privacy Policies: Combining Computational and Legal Perspectives](https://md.research.elamur.ai/free/en/papers/ravichander-2019-question-answering-privacy-policies-combining/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Abhilasha Ravichander, Alan W Black, Shomir Wilson, et al.*2019 · 163 citations*
46. [**Are Natural Language Inference Models IMPPRESsive? Learning IMPlicature and PRESupposition**Paloma Jeretic, Alex Warstadt, Suvrat Bhooshan, et al.*2020 · 161 citations*](https://md.research.elamur.ai/free/en/papers/jeretic-2020-are-natural-language-inference-models/)
47. **[Mind the Trade-off: Debiasing NLU Models without Degrading the In-distribution Performance](https://md.research.elamur.ai/free/en/papers/utama-2020-mind-trade-off-debiasing-nlu/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Prasetya Ajie Utama, Nafise Sadat Moosavi, Iryna Gurevych*2020 · 159 citations*
48. **[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*
49. **[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*
50. [**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/)
51. **[Probing the Probing Paradigm: Does Probing Accuracy Entail Task Relevance?](https://md.research.elamur.ai/free/en/papers/ravichander-2021-probing-probing-paradigm-does-probing/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Abhilasha Ravichander, Yonatan Belinkov, Eduard Hovy*2021 · 144 citations*
52. **[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*
53. [**Recent Advances in Natural Language Inference: A Survey of Benchmarks, Resources, and Approaches arXiv:1904.01172**Shane Storks, Qiaozi Gao, Joyce Y. Chai*2019 · 135 citations*](https://md.research.elamur.ai/free/en/papers/storks-2019-recent-advances-natural-language-inference/)
54. **[FLUTE: Figurative Language Understanding through Textual Explanations](https://md.research.elamur.ai/free/en/papers/chakrabarty-2022-flute-figurative-language-understanding-through/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Tuhin Chakrabarty, Arkadiy Saakyan, Debanjan Ghosh, et al.*2022 · 133 citations*
55. **[Generalization in NLI: Ways (Not) To Go Beyond Simple Heuristics](https://md.research.elamur.ai/free/en/papers/bhargava-2021-generalization-nli-ways-not-go/) [Workshop on Insights from Negative Results in NLP](https://md.research.elamur.ai/free/en/venues/insights/)**Prajjwal Bhargava, Aleksandr Drozd, Anna Rogers*2021 · 131 citations*
56. [**An Analysis of Natural Language Inference Benchmarks through the Lens of Negation**Md Mosharaf Hossain, Venelin Kovatchev, Pranoy Dutta, et al.*2020 · 130 citations*](https://md.research.elamur.ai/free/en/papers/hossain-2020-analysis-natural-language-inference-benchmarks/)
57. **[Don't Take the Premise for Granted: Mitigating Artifacts in Natural Language Inference](https://md.research.elamur.ai/free/en/papers/belinkov-2019-don-t-take-premise-granted/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yonatan Belinkov, Adam Poliak, Stuart Shieber, et al.*2019 · 123 citations*
58. **[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*
59. **[Analyzing Compositionality-Sensitivity of NLI Models](https://md.research.elamur.ai/free/en/papers/nie-2018-analyzing-compositionality-sensitivity-nli-models/) [Proceedings of the AAAI Conference on Artificial Intelligence](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-aaai-conference-on-artificial-intelligence/)**Yixin Nie, Yicheng Wang, Mohit Bansal*2018 · 121 citations*
60. **[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*
61. **[WiCE: Real-World Entailment for Claims in Wikipedia](https://md.research.elamur.ai/free/en/papers/kamoi-2023-wice-real-world-entailment-claims/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Ryo Kamoi, Tanya Goyal, Juan Diego Rodriguez, et al.*2023 · 113 citations*
62. **[Do Androids Laugh at Electric Sheep? Humor “Understanding” Benchmarks from The New Yorker Caption Contest](https://md.research.elamur.ai/free/en/papers/hessel-2023-do-androids-laugh-electric-sheep/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jack Hessel, Ana Marasovic, Jena D. Hwang, et al.*2023 · 112 citations*
63. **[End-to-End Bias Mitigation by Modelling Biases in Corpora](https://md.research.elamur.ai/free/en/papers/mahabadi-2020-end-end-bias-mitigation-modelling/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Rabeeh Karimi Mahabadi, Yonatan Belinkov, James Henderson*2020 · 111 citations*
64. **[Uncertain Natural Language Inference](https://md.research.elamur.ai/free/en/papers/chen-2020-uncertain-natural-language-inference/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Tongfei Chen, Zhengping Jiang, Adam Poliak, et al.*2020 · 107 citations*
65. **[Look at the First Sentence: Position Bias in Question Answering](https://md.research.elamur.ai/free/en/papers/ko-2020-look-first-sentence-position-bias/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Miyoung Ko, Jinhyuk Lee, Hyunjae Kim, et al.*2020 · 104 citations*
66. **[Probing Natural Language Inference Models through Semantic Fragments](https://md.research.elamur.ai/free/en/papers/richardson-2019-probing-natural-language-inference-models/) [Proceedings of the AAAI Conference on Artificial Intelligence](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-aaai-conference-on-artificial-intelligence/)**Kyle Richardson, Hai Hu, Lawrence S. Moss, et al.*2019 · 102 citations*
67. [**A Long Way to Go: Investigating Length Correlations in RLHF arXiv:2310.03716**Prasann Singhal, Tanya Goyal, Jiacheng Xu, et al.*2023 · 99 citations*](https://md.research.elamur.ai/free/en/papers/singhal-2023-long-way-go-investigating-length/)
68. **[Generating Data to Mitigate Spurious Correlations in Natural Language Inference Datasets](https://md.research.elamur.ai/free/en/papers/wu-2022-generating-data-mitigate-spurious-correlations/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yuxiang Wu, Matt Gardner, Pontus Stenetorp, et al.*2022 · 99 citations*
69. **[On Adversarial Removal of Hypothesis-only Bias in Natural Language Inference](https://md.research.elamur.ai/free/en/papers/belinkov-2019-adversarial-removal-hypothesis-only-bias/) [Joint Conference on Lexical and Computational Semantics (*SEM)](https://md.research.elamur.ai/free/en/venues/starsem/)**Yonatan Belinkov, Adam Poliak, Stuart Shieber, et al.*2019 · 96 citations*
70. **[JGLUE: Japanese General Language Understanding Evaluation](https://md.research.elamur.ai/free/en/papers/kurihara-2022-jglue-japanese-general-language-understanding/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Kentaro Kurihara, Daisuke Kawahara, Tomohide Shibata*2022 · 90 citations*
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79. [**Temporal Reasoning in Natural Language Inference**Siddharth Vashishtha, Adam Poliak, Yash Kumar Lal, et al.*2020 · 59 citations*](https://md.research.elamur.ai/free/en/papers/vashishtha-2020-temporal-reasoning-natural-language-inference/)
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88. [**Stress-Testing Neural Models of Natural Language Inference with Multiply-Quantified Sentences arXiv:1810.13033**Atticus Geiger; Ignacio Cases; Lauri Karttunen;, Christopher Potts*2018 · 37 citations*](https://md.research.elamur.ai/free/en/papers/geiger-2018-stress-testing-neural-models-natural/)
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90. **[MedNLI Is Not Immune: Natural Language Inference Artifacts in the Clinical Domain](https://md.research.elamur.ai/free/en/papers/herlihy-2021-mednli-is-not-immune-natural/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Christine Herlihy, Rachel Rudinger*2021 · 30 citations*
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96. [**Beyond Leaderboards: A survey of methods for revealing weaknesses in Natural Language Inference data and models arXiv:2005.14709**Viktor Schlegel, Goran Nenadic, Riza Batista-Navarro*2020 · 24 citations*](https://md.research.elamur.ai/free/en/papers/schlegel-2020-leaderboards-survey-methods-revealing-weaknesses/)
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100. **[When Truth Matters - Addressing Pragmatic Categories in Natural Language Inference (NLI) by Large Language Models (LLMs)](https://md.research.elamur.ai/free/en/papers/gubelmann-2023-when-truth-matters-addressing-pragmatic/) [Joint Conference on Lexical and Computational Semantics (*SEM)](https://md.research.elamur.ai/free/en/venues/starsem/)**Reto Gubelmann, Aikaterini-lida Kalouli, Christina Niklaus, et al.*2023 · 15 citations*

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