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# Who cites «Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference» · page 2

699 papers of our library cite it, newest first: found in their bibliographies, which we read ourselves. [← the card](https://md.mova.elamur.ai/free/en/docs/papers/mccoy-2019-right-wrong-reasons-diagnosing-syntactic/)

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1. [Toward the Evaluation of Large Language Models Considering Score Variance across Instruction Templates](https://aclanthology.org/2024.blackboxnlp-1.31/) *— 2024 · Proceedings of the 7th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP*
2. [Towards Faithful Model Explanation in NLP: A Survey](https://aclanthology.org/2024.cl-2.6/) *— 2024 · Computational Linguistics*
3. [Towards a Framework for Evaluating Explanations in Automated Fact Verification](https://aclanthology.org/2024.lrec-main.1422/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
4. [Understanding and Mitigating Spurious Correlations in Text Classification with Neighborhood Analysis](https://aclanthology.org/2024.findings-eacl.68/) *— 2024 · Findings of the Association for Computational Linguistics: EACL 2024*
5. [Using Natural Language Explanations to Improve Robustness of In-context Learning](https://aclanthology.org/2024.acl-long.728/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
6. [Whispers of Doubt Amidst Echoes of Triumph in NLP Robustness](https://aclanthology.org/2024.naacl-long.310/) *— 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
7. [tasksource: A Large Collection of NLP tasks with a Structured Dataset Preprocessing Framework](https://aclanthology.org/2024.lrec-main.1361/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
8. [A Close Look into the Calibration of Pre-trained Language Models](https://aclanthology.org/2023.acl-long.75/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
9. [A Comprehensive Overview of Large Language Models](https://arxiv.org/abs/2307.06435) *— 2023 · arXiv:2307.06435*
10. [A fine-grained comparison of pragmatic language understanding in humans and language models](https://md.mova.elamur.ai/free/en/docs/papers/hu-2023-fine-grained-comparison-pragmatic-language/) *— our card · 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
11. [An Empirical Survey of Data Augmentation for Limited Data Learning in NLP](https://aclanthology.org/2023.tacl-1.12/) *— 2023 · Transactions of the Association for Computational Linguistics*
12. [Are Sample-Efficient NLP Models More Robust?](https://aclanthology.org/2023.acl-short.144/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)*
13. [Assessing Monotonicity Reasoning in Dutch through Natural Language Inference](https://aclanthology.org/2023.findings-eacl.110/) *— 2023 · Findings of the Association for Computational Linguistics: EACL 2023*
14. [Assessing Out-of-Domain Language Model Performance from Few Examples](https://aclanthology.org/2023.eacl-main.175/) *— 2023 · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics*
15. [Beat LLMs at Their Own Game: Zero-Shot LLM-Generated Text Detection via Querying ChatGPT](https://aclanthology.org/2023.emnlp-main.463/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
16. [Being Right for Whose Right Reasons?](https://aclanthology.org/2023.acl-long.59/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
17. [Beyond Testers'' Biases: Guiding Model Testing with Knowledge Bases using LLMs](https://aclanthology.org/2023.findings-emnlp.901/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
18. [CAR: Conceptualization-Augmented Reasoner for Zero-Shot Commonsense Question Answering](https://aclanthology.org/2023.findings-emnlp.902/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
19. [Can current NLI systems handle German word order? Investigating language model performance on a new German challenge set of minimal pairs](https://aclanthology.org/2023.iwcs-1.1/) *— 2023 · Proceedings of the 15th International Conference on Computational Semantics*
20. [Counterfactual Active Learning for Out-of-Distribution Generalization](https://aclanthology.org/2023.acl-long.636/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
21. [Curing the SICK and Other NLI Maladies](https://aclanthology.org/2023.cl-1.5/) *— 2023 · Computational Linguistics*
22. [DISCO: Distilling Counterfactuals with Large Language Models](https://aclanthology.org/2023.acl-long.302/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
23. [Data Contamination Through the Lens of Time](https://arxiv.org/abs/2310.10628) *— 2023 · arXiv:2310.10628*
24. [DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models](https://arxiv.org/abs/2306.11698) *— 2023 · arXiv:2306.11698*
25. [Dissociating language and thought in large language models](https://arxiv.org/abs/2301.06627) *— 2023 · arXiv:2301.06627*
26. [Do large language models resemble humans in language use?](https://arxiv.org/abs/2303.08014) *— 2023 · arXiv:2303.08014*
27. [Embers of Autoregression: Understanding Large Language Models Through the Problem They are Trained to Solve](https://arxiv.org/abs/2309.13638) *— 2023 · arXiv:2309.13638*
28. [Empower Nested Boolean Logic via Self-Supervised Curriculum Learning](https://aclanthology.org/2023.emnlp-main.847/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
29. [Empowering Cross-lingual Behavioral Testing of NLP Models with Typological Features](https://aclanthology.org/2023.acl-long.396/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
30. [Establishing Trustworthiness: Rethinking Tasks and Model Evaluation](https://aclanthology.org/2023.emnlp-main.14/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
31. [Factually Consistent Summarization via Reinforcement Learning with Textual Entailment Feedback](https://aclanthology.org/2023.acl-long.344/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
32. [Few-shot Fine-tuning vs. In-context Learning: A Fair Comparison and Evaluation](https://aclanthology.org/2023.findings-acl.779/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
33. [Fighting Bias With Bias: Promoting Model Robustness by Amplifying Dataset Biases](https://aclanthology.org/2023.findings-acl.833/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
34. [GLUE-X: Evaluating Natural Language Understanding Models from an Out-of-Distribution Generalization Perspective](https://aclanthology.org/2023.findings-acl.806/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
35. [GPT-4 Can't Reason](https://arxiv.org/abs/2308.03762) *— 2023 · arXiv:2308.03762*
36. [Generative Models as a Complex Systems Science: How can we make sense of large language model behavior?](https://arxiv.org/abs/2308.00189) *— 2023 · arXiv:2308.00189*
37. [How Well Do Text Embedding Models Understand Syntax?](https://aclanthology.org/2023.findings-emnlp.650/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
38. [How to Plant Trees in Language Models: Data and Architectural Effects on the Emergence of Syntactic Inductive Biases](https://arxiv.org/abs/2305.19905) *— 2023 · arXiv:2305.19905*
39. [HuaSLIM: Human Attention Motivated Shortcut Learning Identification and Mitigation for Large Language models](https://aclanthology.org/2023.findings-acl.781/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
40. [IAEval: A Comprehensive Evaluation of Instance Attribution on Natural Language Understanding](https://aclanthology.org/2023.findings-emnlp.801/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
41. [Identifying the limits of transformers when performing model-checking with natural language](https://aclanthology.org/2023.eacl-main.257/) *— 2023 · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics*
42. [Impact of Co-occurrence on Factual Knowledge of Large Language Models](https://aclanthology.org/2023.findings-emnlp.518/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
43. [Improving Bias Mitigation through Bias Experts in Natural Language Understanding](https://aclanthology.org/2023.emnlp-main.681/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
44. [Improving the robustness of NLI models with minimax training](https://aclanthology.org/2023.acl-long.801/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
45. [Investigating UD Treebanks via Dataset Difficulty Measures](https://aclanthology.org/2023.eacl-main.76/) *— 2023 · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics*
46. [It Takes Two to Tango: Navigating Conceptualizations of NLP Tasks and Measurements of Performance](https://aclanthology.org/2023.findings-acl.202/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
47. [Jamp: Controlled Japanese Temporal Inference Dataset for Evaluating Generalization Capacity of Language Models](https://aclanthology.org/2023.acl-srw.8/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)*
48. [LMentry: A Language Model Benchmark of Elementary Language Tasks](https://aclanthology.org/2023.findings-acl.666/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
49. [Language Generation Models Can Cause Harm: So What Can We Do About It? An Actionable Survey](https://aclanthology.org/2023.eacl-main.241/) *— 2023 · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics*
50. [Large Language Models Can be Lazy Learners: Analyze Shortcuts in In-Context Learning](https://aclanthology.org/2023.findings-acl.284/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
51. [Measuring Inductive Biases of In-Context Learning with Underspecified Demonstrations](https://aclanthology.org/2023.acl-long.632/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
52. [Measuring the Knowledge Acquisition-Utilization Gap in Pretrained Language Models](https://aclanthology.org/2023.findings-emnlp.285/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
53. [Mind the Labels: Describing Relations in Knowledge Graphs With Pretrained Models](https://aclanthology.org/2023.eacl-main.176/) *— 2023 · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics*
54. [Mind the instructions: a holistic evaluation of consistency and interactions in prompt-based learning](https://aclanthology.org/2023.conll-1.20/) *— 2023 · Proceedings of the 27th Conference on Computational Natural Language Learning (CoNLL)*
55. [Mitigating Backdoor Poisoning Attacks through the Lens of Spurious Correlation](https://aclanthology.org/2023.emnlp-main.60/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
56. [Mitigating Biases in Hate Speech Detection from A Causal Perspective](https://aclanthology.org/2023.findings-emnlp.440/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
57. [NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation](https://nejlt.ep.liu.se/article/view/4725) *— 2023 · Northern European Journal of Language Technology*
58. [NLMs: Augmenting Negation in Language Models](https://aclanthology.org/2023.findings-emnlp.873/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
59. [On the Blind Spots of Model-Based Evaluation Metrics for Text Generation](https://aclanthology.org/2023.acl-long.674/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
60. [Out-of-Distribution Generalization in Natural Language Processing: Past, Present, and Future](https://aclanthology.org/2023.emnlp-main.276/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
61. [PECO: Examining Single Sentence Label Leakage in Natural Language Inference Datasets through Progressive Evaluation of Cluster Outliers](https://aclanthology.org/2023.eacl-main.223/) *— 2023 · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics*
62. [PROPRES: Investigating the Projectivity of Presupposition with Various Triggers and Environments](https://aclanthology.org/2023.conll-1.9/) *— 2023 · Proceedings of the 27th Conference on Computational Natural Language Learning (CoNLL)*
63. [PreCog: Exploring the Relation between Memorization and Performance in Pre-trained Language Models](https://aclanthology.org/2023.ranlp-1.103/) *— 2023 · Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing*
64. [Prompting for explanations improves Adversarial NLI. Is this true? \Yes\ it is \true\ because \it weakens superficial cues\](https://aclanthology.org/2023.findings-eacl.162/) *— 2023 · Findings of the Association for Computational Linguistics: EACL 2023*
65. [Pushing the Limits of ChatGPT on NLP Tasks](https://arxiv.org/abs/2306.09719) *— 2023 · arXiv:2306.09719*
66. [Robust Natural Language Understanding with Residual Attention Debiasing](https://aclanthology.org/2023.findings-acl.32/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
67. [Robust Prompt Optimization for Large Language Models Against Distribution Shifts](https://aclanthology.org/2023.emnlp-main.95/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
68. [Robustness Challenges in Model Distillation and Pruning for Natural Language Understanding](https://aclanthology.org/2023.eacl-main.129/) *— 2023 · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics*
69. [SETI: Systematicity Evaluation of Textual Inference](https://aclanthology.org/2023.findings-acl.252/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
70. [ScoNe: Benchmarking Negation Reasoning in Language Models With Fine-Tuning and In-Context Learning](https://aclanthology.org/2023.acl-short.154/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)*
71. [Stubborn Lexical Bias in Data and Models](https://aclanthology.org/2023.findings-acl.516/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
72. [Sub-Character Tokenization for Chinese Pretrained Language Models](https://aclanthology.org/2023.tacl-1.28/) *— 2023 · Transactions of the Association for Computational Linguistics*
73. [Towards Stable Natural Language Understanding via Information Entropy Guided Debiasing](https://aclanthology.org/2023.acl-long.161/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
74. [Trained on 100 million words and still in shape: BERT meets British National Corpus](https://aclanthology.org/2023.findings-eacl.146/) *— 2023 · Findings of the Association for Computational Linguistics: EACL 2023*
75. [Translated Benchmarks Can Be Misleading: the Case of Estonian Question Answering](https://aclanthology.org/2023.nodalida-1.71/) *— 2023 · Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa)*
76. [Understanding the Inner-workings of Language Models Through Representation Dissimilarity](https://aclanthology.org/2023.emnlp-main.403/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
77. [Unifying Large Language Models and Knowledge Graphs: A Roadmap](https://arxiv.org/abs/2306.08302) *— 2023 · arXiv:2306.08302 · IEEE Transactions on Knowledge and Data Engineering (TKDE) 2024*
78. [What Makes Chain-of-Thought Prompting Effective? A Counterfactual Study](https://aclanthology.org/2023.findings-emnlp.101/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
79. [What does the Failure to Reason with ``Respectively'' in Zero/Few-Shot Settings Tell Us about Language Models?](https://aclanthology.org/2023.acl-long.489/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
80. [What's the Meaning of Superhuman Performance in Today's NLU?](https://aclanthology.org/2023.acl-long.697/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
81. [When and Why Does Bias Mitigation Work?](https://aclanthology.org/2023.findings-emnlp.619/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
82. [Which Spurious Correlations Impact Reasoning in NLI Models? A Visual Interactive Diagnosis through Data-Constrained Counterfactuals](https://aclanthology.org/2023.acl-demo.44/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)*
83. [WiCE: Real-World Entailment for Claims in Wikipedia](https://aclanthology.org/2023.emnlp-main.470/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
84. [A Generative Approach for Mitigating Structural Biases in Natural Language Inference](https://aclanthology.org/2022.starsem-1.16/) *— 2022 · Proceedings of the 11th Joint Conference on Lexical and Computational Semantics*
85. [A Philosophically-Informed Contribution to the Generalization Problem of Neural Natural Language Inference: Shallow Heuristics, Bias, and the Varieties of Inference](https://aclanthology.org/2022.naloma-1.5/) *— 2022 · Proceedings of the 3rd Natural Logic Meets Machine Learning Workshop (NALOMA III)*
86. [A Report on the FigLang 2022 Shared Task on Understanding Figurative Language](https://aclanthology.org/2022.flp-1.26/) *— 2022 · Proceedings of the 3rd Workshop on Figurative Language Processing (FLP)*
87. [A Survey on Automated Fact-Checking](https://aclanthology.org/2022.tacl-1.11/) *— 2022 · Transactions of the Association for Computational Linguistics*
88. [ANLIzing the Adversarial Natural Language Inference Dataset](https://aclanthology.org/2022.scil-1.3/) *— 2022 · Proceedings of the Society for Computation in Linguistics 2022*
89. [Adaptor: Objective-Centric Adaptation Framework for Language Models](https://aclanthology.org/2022.acl-demo.26/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations*
90. [Aligning Sentences in a Paragraph-Paraphrased Corpus with New Embedding-based Similarity Measures](https://aclanthology.org/2022.ijclclp-2.1/) *— 2022 · International Journal of Computational Linguistics & Chinese Language Processing, Volume 27, Number 2, December 2022*
91. [An Investigation of the (In)effectiveness of Counterfactually Augmented Data](https://aclanthology.org/2022.acl-long.256/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
92. [AnaLog: Testing Analytical and Deductive Logic Learnability in Language Models](https://aclanthology.org/2022.starsem-1.5/) *— 2022 · Proceedings of the 11th Joint Conference on Lexical and Computational Semantics*
93. [Analyzing Dynamic Adversarial Training Data in the Limit](https://aclanthology.org/2022.findings-acl.18/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
94. [Analyzing Multi-Task Learning for Abstractive Text Summarization](https://aclanthology.org/2022.gem-1.5/) *— 2022 · Proceedings of the Second Workshop on Natural Language Generation, Evaluation, and Metrics (GEM)*
95. [Are All Spurious Features in Natural Language Alike? An Analysis through a Causal Lens](https://aclanthology.org/2022.emnlp-main.666/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
96. [Are Prompt-based Models Clueless?](https://aclanthology.org/2022.acl-long.166/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
97. [Attack on Unfair ToS Clause Detection: A Case Study using Universal Adversarial Triggers](https://aclanthology.org/2022.nllp-1.21/) *— 2022 · Proceedings of the Natural Legal Language Processing Workshop 2022*
98. [Beyond Distributional Hypothesis: Let Language Models Learn Meaning-Text Correspondence](https://aclanthology.org/2022.findings-naacl.156/) *— 2022 · Findings of the Association for Computational Linguistics: NAACL 2022*
99. [Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models](https://arxiv.org/abs/2206.04615) *— 2022 · arXiv:2206.04615 · Transactions on Machine Learning Research, May/2022, https://openreview.net/forum?id=uyTL5Bvosj*
100. [CILDA: Contrastive Data Augmentation Using Intermediate Layer Knowledge Distillation](https://aclanthology.org/2022.coling-1.417/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*

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