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home / docs / papers / cited-by / Who cites «Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference» · page 2

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

Pages: 1 · 2 · 3 · 4 · 5 · 6 · 7

  1. Toward the Evaluation of Large Language Models Considering Score Variance across Instruction Templates — 2024 · Proceedings of the 7th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP
  2. Towards Faithful Model Explanation in NLP: A Survey — 2024 · Computational Linguistics
  3. Towards a Framework for Evaluating Explanations in Automated Fact Verification — 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 — 2024 · Findings of the Association for Computational Linguistics: EACL 2024
  5. Using Natural Language Explanations to Improve Robustness of In-context Learning — 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 — 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 — 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 — 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 — 2023 · arXiv:2307.06435
  10. A fine-grained comparison of pragmatic language understanding in humans and language models — 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 — 2023 · Transactions of the Association for Computational Linguistics
  12. Are Sample-Efficient NLP Models More Robust? — 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 — 2023 · Findings of the Association for Computational Linguistics: EACL 2023
  14. Assessing Out-of-Domain Language Model Performance from Few Examples — 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 — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
  16. Being Right for Whose Right Reasons? — 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 — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  18. CAR: Conceptualization-Augmented Reasoner for Zero-Shot Commonsense Question Answering — 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 — 2023 · Proceedings of the 15th International Conference on Computational Semantics
  20. Counterfactual Active Learning for Out-of-Distribution Generalization — 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 — 2023 · Computational Linguistics
  22. DISCO: Distilling Counterfactuals with Large Language Models — 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 — 2023 · arXiv:2310.10628
  24. DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models — 2023 · arXiv:2306.11698
  25. Dissociating language and thought in large language models — 2023 · arXiv:2301.06627
  26. Do large language models resemble humans in language use? — 2023 · arXiv:2303.08014
  27. Embers of Autoregression: Understanding Large Language Models Through the Problem They are Trained to Solve — 2023 · arXiv:2309.13638
  28. Empower Nested Boolean Logic via Self-Supervised Curriculum Learning — 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 — 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 — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
  31. Factually Consistent Summarization via Reinforcement Learning with Textual Entailment Feedback — 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 — 2023 · Findings of the Association for Computational Linguistics: ACL 2023
  33. Fighting Bias With Bias: Promoting Model Robustness by Amplifying Dataset Biases — 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 — 2023 · Findings of the Association for Computational Linguistics: ACL 2023
  35. GPT-4 Can't Reason — 2023 · arXiv:2308.03762
  36. Generative Models as a Complex Systems Science: How can we make sense of large language model behavior? — 2023 · arXiv:2308.00189
  37. How Well Do Text Embedding Models Understand Syntax? — 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 — 2023 · arXiv:2305.19905
  39. HuaSLIM: Human Attention Motivated Shortcut Learning Identification and Mitigation for Large Language models — 2023 · Findings of the Association for Computational Linguistics: ACL 2023
  40. IAEval: A Comprehensive Evaluation of Instance Attribution on Natural Language Understanding — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  41. Identifying the limits of transformers when performing model-checking with natural language — 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 — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  43. Improving Bias Mitigation through Bias Experts in Natural Language Understanding — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
  44. Improving the robustness of NLI models with minimax training — 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 — 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 — 2023 · Findings of the Association for Computational Linguistics: ACL 2023
  47. Jamp: Controlled Japanese Temporal Inference Dataset for Evaluating Generalization Capacity of Language Models — 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 — 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 — 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 — 2023 · Findings of the Association for Computational Linguistics: ACL 2023
  51. Measuring Inductive Biases of In-Context Learning with Underspecified Demonstrations — 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 — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  53. Mind the Labels: Describing Relations in Knowledge Graphs With Pretrained Models — 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 — 2023 · Proceedings of the 27th Conference on Computational Natural Language Learning (CoNLL)
  55. Mitigating Backdoor Poisoning Attacks through the Lens of Spurious Correlation — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
  56. Mitigating Biases in Hate Speech Detection from A Causal Perspective — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  57. NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation — 2023 · Northern European Journal of Language Technology
  58. NLMs: Augmenting Negation in Language Models — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  59. On the Blind Spots of Model-Based Evaluation Metrics for Text Generation — 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 — 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 — 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 — 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 — 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\ — 2023 · Findings of the Association for Computational Linguistics: EACL 2023
  65. Pushing the Limits of ChatGPT on NLP Tasks — 2023 · arXiv:2306.09719
  66. Robust Natural Language Understanding with Residual Attention Debiasing — 2023 · Findings of the Association for Computational Linguistics: ACL 2023
  67. Robust Prompt Optimization for Large Language Models Against Distribution Shifts — 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 — 2023 · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics
  69. SETI: Systematicity Evaluation of Textual Inference — 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 — 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 — 2023 · Findings of the Association for Computational Linguistics: ACL 2023
  72. Sub-Character Tokenization for Chinese Pretrained Language Models — 2023 · Transactions of the Association for Computational Linguistics
  73. Towards Stable Natural Language Understanding via Information Entropy Guided Debiasing — 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 — 2023 · Findings of the Association for Computational Linguistics: EACL 2023
  75. Translated Benchmarks Can Be Misleading: the Case of Estonian Question Answering — 2023 · Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa)
  76. Understanding the Inner-workings of Language Models Through Representation Dissimilarity — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
  77. Unifying Large Language Models and Knowledge Graphs: A Roadmap — 2023 · arXiv:2306.08302 · IEEE Transactions on Knowledge and Data Engineering (TKDE) 2024
  78. What Makes Chain-of-Thought Prompting Effective? A Counterfactual Study — 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? — 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? — 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? — 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 — 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 — 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 — 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 — 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 — 2022 · Proceedings of the 3rd Workshop on Figurative Language Processing (FLP)
  87. A Survey on Automated Fact-Checking — 2022 · Transactions of the Association for Computational Linguistics
  88. ANLIzing the Adversarial Natural Language Inference Dataset — 2022 · Proceedings of the Society for Computation in Linguistics 2022
  89. Adaptor: Objective-Centric Adaptation Framework for Language Models — 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 — 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 — 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 — 2022 · Proceedings of the 11th Joint Conference on Lexical and Computational Semantics
  93. Analyzing Dynamic Adversarial Training Data in the Limit — 2022 · Findings of the Association for Computational Linguistics: ACL 2022
  94. Analyzing Multi-Task Learning for Abstractive Text Summarization — 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 — 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
  96. Are Prompt-based Models Clueless? — 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 — 2022 · Proceedings of the Natural Legal Language Processing Workshop 2022
  98. Beyond Distributional Hypothesis: Let Language Models Learn Meaning-Text Correspondence — 2022 · Findings of the Association for Computational Linguistics: NAACL 2022
  99. Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models — 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 — 2022 · Proceedings of the 29th International Conference on Computational Linguistics

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