---
title: "Who cites Abductive Commonsense Reasoning — page 2"
description: "The papers of the Elamur Research library that cite «Abductive Commonsense Reasoning», found in their bibliographies."
canonical: "https://research.elamur.ai/free/en/papers/bhagavatula-2019-abductive-commonsense-reasoning/cited-by/page/2/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/bhagavatula-2019-abductive-commonsense-reasoning/cited-by/page/2/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Abductive Commonsense Reasoning](https://md.research.elamur.ai/free/en/papers/bhagavatula-2019-abductive-commonsense-reasoning/) / cited by

# Who cites «Abductive Commonsense Reasoning» · page 2

274 papers of our library cite it, newest first: found in their bibliographies, which we read ourselves. [← the card](https://md.research.elamur.ai/free/en/papers/bhagavatula-2019-abductive-commonsense-reasoning/)

Pages: [1](https://md.research.elamur.ai/free/en/papers/bhagavatula-2019-abductive-commonsense-reasoning/cited-by/) · **2** · [3](https://md.research.elamur.ai/free/en/papers/bhagavatula-2019-abductive-commonsense-reasoning/cited-by/page/3/)

1. [A Systematic Study of Knowledge Distillation for Natural Language Generation with Pseudo-Target Training](https://aclanthology.org/2023.acl-long.818/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
2. [A Textual Dataset for Situated Proactive Response Selection](https://aclanthology.org/2023.acl-long.214/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
3. [Abductive Commonsense Reasoning Exploiting Mutually Exclusive Explanations](https://aclanthology.org/2023.acl-long.831/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
4. [Are Large Language Models Really Good Logical Reasoners? A Comprehensive Evaluation and Beyond](https://arxiv.org/abs/2306.09841) *— 2023 · arXiv:2306.09841*
5. [CALM-Bench: A Multi-task Benchmark for Evaluating Causality-Aware Language Models](https://aclanthology.org/2023.findings-eacl.23/) *— 2023 · Findings of the Association for Computational Linguistics: EACL 2023*
6. [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*
7. [CAT: A Contextualized Conceptualization and Instantiation Framework for Commonsense Reasoning](https://aclanthology.org/2023.acl-long.733/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
8. [CLadder: Assessing Causal Reasoning in Language Models](https://arxiv.org/abs/2312.04350) *— 2023 · arXiv:2312.04350*
9. [COGEN: Abductive Commonsense Language Generation](https://aclanthology.org/2023.acl-short.26/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)*
10. [COLA: Contextualized Commonsense Causal Reasoning from the Causal Inference Perspective](https://aclanthology.org/2023.acl-long.288/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
11. [CReTIHC: Designing Causal Reasoning Tasks about Temporal Interventions and Hallucinated Confoundings](https://aclanthology.org/2023.findings-emnlp.693/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
12. [CRoW: Benchmarking Commonsense Reasoning in Real-World Tasks](https://aclanthology.org/2023.emnlp-main.607/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
13. [Can Large Language Models Infer Causation from Correlation?](https://arxiv.org/abs/2306.05836) *— 2023 · arXiv:2306.05836*
14. [Cognitive Reframing of Negative Thoughts through Human-Language Model Interaction](https://aclanthology.org/2023.acl-long.555/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
15. [Commonsense Knowledge Transfer for Pre-trained Language Models](https://aclanthology.org/2023.findings-acl.368/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
16. [Commonsense Reasoning for Conversational AI: A Survey of the State of the Art](https://arxiv.org/abs/2302.07926) *— 2023 · arXiv:2302.07926*
17. [Contrastive Learning for Inference in Dialogue](https://aclanthology.org/2023.emnlp-main.631/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
18. [Crystal: Introspective Reasoners Reinforced with Self-Feedback](https://aclanthology.org/2023.emnlp-main.708/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
19. [Curating Datasets for Better Performance with Example Training Dynamics](https://aclanthology.org/2023.findings-acl.674/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
20. [Curing the SICK and Other NLI Maladies](https://aclanthology.org/2023.cl-1.5/) *— 2023 · Computational Linguistics*
21. [Defining a New NLP Playground](https://aclanthology.org/2023.findings-emnlp.799/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
22. [Disentangling Reasoning Capabilities from Language Models with Compositional Reasoning Transformers](https://aclanthology.org/2023.findings-acl.480/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
23. [Evaluating and Improving the Coreference Capabilities of Machine Translation Models](https://aclanthology.org/2023.eacl-main.69/) *— 2023 · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics*
24. [Evaluating the Rationale Understanding of Critical Reasoning in Logical Reading Comprehension](https://aclanthology.org/2023.emnlp-main.9/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
25. [Examining Inter-Consistency of Large Language Models Collaboration: An In-depth Analysis via Debate](https://aclanthology.org/2023.findings-emnlp.508/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
26. [FLASK: Fine-grained Language Model Evaluation based on Alignment Skill Sets](https://arxiv.org/abs/2307.10928) *— 2023 · arXiv:2307.10928*
27. [Few-shot Adaptation Works with UnpredicTable Data](https://aclanthology.org/2023.acl-long.102/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
28. [Fill in the Blank: Exploring and Enhancing LLM Capabilities for Backward Reasoning in Math Word Problems](https://arxiv.org/abs/2310.01991) *— 2023 · arXiv:2310.01991*
29. [Gold: A Global and Local-aware Denoising Framework for Commonsense Knowledge Graph Noise Detection](https://aclanthology.org/2023.findings-emnlp.232/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
30. [Human-like Few-Shot Learning via Bayesian Reasoning over Natural Language](https://arxiv.org/abs/2306.02797) *— 2023 · arXiv:2306.02797*
31. [JECC: Commonsense Reasoning Tasks Derived from Interactive Fictions](https://aclanthology.org/2023.findings-acl.713/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
32. [Knowledge Graph Compression Enhances Diverse Commonsense Generation](https://aclanthology.org/2023.emnlp-main.37/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
33. [Knowledge Rumination for Pre-trained Language Models](https://aclanthology.org/2023.emnlp-main.206/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
34. [Large Language Models are Better Reasoners with Self-Verification](https://aclanthology.org/2023.findings-emnlp.167/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
35. [Large Language Models for Generative Information Extraction: A Survey](https://arxiv.org/abs/2312.17617) *— 2023 · arXiv:2312.17617*
36. [Learning to Initialize: Can Meta Learning Improve Cross-task Generalization in Prompt Tuning?](https://aclanthology.org/2023.acl-long.659/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
37. [Learning to Predict Task Transferability via Soft Prompt](https://aclanthology.org/2023.emnlp-main.546/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
38. [Less Likely Brainstorming: Using Language Models to Generate Alternative Hypotheses](https://aclanthology.org/2023.findings-acl.794/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
39. [Logical Reasoning over Natural Language as Knowledge Representation: A Survey](https://arxiv.org/abs/2303.12023) *— 2023 · arXiv:2303.12023*
40. [Making Language Models Better Reasoners with Step-Aware Verifier](https://aclanthology.org/2023.acl-long.291/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
41. [Multi-modal Action Chain Abductive Reasoning](https://aclanthology.org/2023.acl-long.254/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
42. [Natural Language Reasoning, A Survey](https://arxiv.org/abs/2303.14725) *— 2023 · arXiv:2303.14725*
43. [NovaCOMET: Open Commonsense Foundation Models with Symbolic Knowledge Distillation](https://aclanthology.org/2023.findings-emnlp.80/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
44. [On Degrees of Freedom in Defining and Testing Natural Language Understanding](https://aclanthology.org/2023.findings-acl.861/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
45. [PASTA: A Dataset for Modeling PArticipant STAtes in Narratives](https://aclanthology.org/2023.tacl-1.73/) *— 2023 · Transactions of the Association for Computational Linguistics*
46. [Pre-Training to Learn in Context](https://aclanthology.org/2023.acl-long.267/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
47. [RECKONING: Reasoning through Dynamic Knowledge Encoding](https://arxiv.org/abs/2305.06349) *— 2023 · arXiv:2305.06349*
48. [SETI: Systematicity Evaluation of Textual Inference](https://aclanthology.org/2023.findings-acl.252/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
49. [Self-Consistent Narrative Prompts on Abductive Natural Language Inference](https://aclanthology.org/2023.ijcnlp-main.67/) *— 2023 · Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)*
50. [The Generative AI Paradox: "What It Can Create, It May Not Understand](https://arxiv.org/abs/2311.00059) *— 2023 · arXiv:2311.00059*
51. [The Magic of IF: Investigating Causal Reasoning Abilities in Large Language Models of Code](https://aclanthology.org/2023.findings-acl.574/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
52. [The Web Can Be Your Oyster for Improving Language Models](https://aclanthology.org/2023.findings-acl.46/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
53. [Towards LogiGLUE: A Brief Survey and A Benchmark for Analyzing Logical Reasoning Capabilities of Language Models](https://arxiv.org/abs/2310.00836) *— 2023 · arXiv:2310.00836*
54. [True Detective: A Deep Abductive Reasoning Benchmark Undoable for GPT-3 and Challenging for GPT-4](https://aclanthology.org/2023.starsem-1.28/) *— 2023 · Proceedings of the 12th Joint Conference on Lexical and Computational Semantics (*SEM 2023)*
55. [Uncovering Implicit Inferences for Improved Relational Argument Mining](https://aclanthology.org/2023.eacl-main.182/) *— 2023 · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics*
56. [Unveiling the Multi-Annotation Process: Examining the Influence of Annotation Quantity and Instance Difficulty on Model Performance](https://aclanthology.org/2023.findings-emnlp.96/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
57. [Vera: A General-Purpose Plausibility Estimation Model for Commonsense Statements](https://aclanthology.org/2023.emnlp-main.81/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
58. [A Critical Reflection and Forward Perspective on Empathy and Natural Language Processing](https://aclanthology.org/2022.findings-emnlp.157/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
59. [A Review on Language Models as Knowledge Bases](https://arxiv.org/abs/2204.06031) *— 2022 · arXiv:2204.06031*
60. [A Systematic Investigation of Commonsense Knowledge in Large Language Models](https://aclanthology.org/2022.emnlp-main.812/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
61. [ACENet: Attention Guided Commonsense Reasoning on Hybrid Knowledge Graph](https://aclanthology.org/2022.emnlp-main.579/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
62. [ANLIzing the Adversarial Natural Language Inference Dataset](https://aclanthology.org/2022.scil-1.3/) *— 2022 · Proceedings of the Society for Computation in Linguistics 2022*
63. [AbductionRules: Training Transformers to Explain Unexpected Inputs](https://aclanthology.org/2022.findings-acl.19/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
64. [Aligning Generative Language Models with Human Values](https://aclanthology.org/2022.findings-naacl.18/) *— 2022 · Findings of the Association for Computational Linguistics: NAACL 2022*
65. [Analytical Reasoning of Text](https://aclanthology.org/2022.findings-naacl.177/) *— 2022 · Findings of the Association for Computational Linguistics: NAACL 2022*
66. [Analyzing Biases to Spurious Correlations in Text Classification Tasks](https://aclanthology.org/2022.aacl-short.11/) *— 2022 · Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)*
67. [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*
68. [CICERO: A Dataset for Contextualized Commonsense Inference in Dialogues](https://aclanthology.org/2022.acl-long.344/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
69. [Case-Based Abductive Natural Language Inference](https://aclanthology.org/2022.coling-1.134/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
70. [ClarET: Pre-training a Correlation-Aware Context-To-Event Transformer for Event-Centric Generation and Classification](https://aclanthology.org/2022.acl-long.183/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
71. [Cloze Evaluation for Deeper Understanding of Commonsense Stories in Indonesian](https://aclanthology.org/2022.csrr-1.2/) *— 2022 · Proceedings of the First Workshop on Commonsense Representation and Reasoning (CSRR 2022)*
72. [CoSe-Co: Text Conditioned Generative CommonSense Contextualizer](https://aclanthology.org/2022.naacl-main.83/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
73. [Coalescing Global and Local Information for Procedural Text Understanding](https://aclanthology.org/2022.coling-1.132/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
74. [Diffusion-LM Improves Controllable Text Generation](https://arxiv.org/abs/2205.14217) *— 2022 · arXiv:2205.14217*
75. [DiscoSense: Commonsense Reasoning with Discourse Connectives](https://aclanthology.org/2022.emnlp-main.703/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
76. [Distributed NLI: Learning to Predict Human Opinion Distributions for Language Reasoning](https://aclanthology.org/2022.findings-acl.79/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
77. [Diversifying Content Generation for Commonsense Reasoning with Mixture of Knowledge Graph Experts](https://aclanthology.org/2022.findings-acl.149/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
78. [Eliciting and Understanding Cross-task Skills with Task-level Mixture-of-Experts](https://aclanthology.org/2022.findings-emnlp.189/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
79. [Embarrassingly Simple Performance Prediction for Abductive Natural Language Inference](https://aclanthology.org/2022.naacl-main.441/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
80. [Forging Multiple Training Objectives for Pre-trained Language Models via Meta-Learning](https://aclanthology.org/2022.findings-emnlp.482/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
81. [GEMv2: Multilingual NLG Benchmarking in a Single Line of Code](https://aclanthology.org/2022.emnlp-demos.27/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations*
82. [GENIE: Toward Reproducible and Standardized Human Evaluation for Text Generation](https://aclanthology.org/2022.emnlp-main.787/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
83. [GraDA: Graph Generative Data Augmentation for Commonsense Reasoning](https://aclanthology.org/2022.coling-1.397/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
84. [Hyperdecoders: Instance-specific decoders for multi-task NLP](https://aclanthology.org/2022.findings-emnlp.124/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
85. [ILDAE: Instance-Level Difficulty Analysis of Evaluation Data](https://aclanthology.org/2022.acl-long.240/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
86. [Knowledge-Augmented Methods for Natural Language Processing](https://aclanthology.org/2022.acl-tutorials.3/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts*
87. [Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-Thought](https://arxiv.org/abs/2210.01240) *— 2022 · arXiv:2210.01240*
88. [Leveraging Large Language Models for Multiple Choice Question Answering](https://arxiv.org/abs/2210.12353) *— 2022 · arXiv:2210.12353*
89. [LogicNMR: Probing the Non-monotonic Reasoning Ability of Pre-trained Language Models](https://aclanthology.org/2022.findings-emnlp.265/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
90. [Modeling Hierarchical Reasoning Chains by Linking Discourse Units and Key Phrases for Reading Comprehension](https://aclanthology.org/2022.coling-1.126/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
91. [Modularized Transfer Learning with Multiple Knowledge Graphs for Zero-shot Commonsense Reasoning](https://aclanthology.org/2022.naacl-main.163/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
92. [Natural Language Deduction with Incomplete Information](https://aclanthology.org/2022.emnlp-main.564/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
93. [On Curriculum Learning for Commonsense Reasoning](https://aclanthology.org/2022.naacl-main.72/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
94. [On the Limitations of Dataset Balancing: The Lost Battle Against Spurious Correlations](https://aclanthology.org/2022.findings-naacl.168/) *— 2022 · Findings of the Association for Computational Linguistics: NAACL 2022*
95. [PaCo: Preconditions Attributed to Commonsense Knowledge](https://aclanthology.org/2022.findings-emnlp.505/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
96. [Possible Stories: Evaluating Situated Commonsense Reasoning under Multiple Possible Scenarios](https://aclanthology.org/2022.coling-1.319/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
97. [Reasoning Like Program Executors](https://aclanthology.org/2022.emnlp-main.48/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
98. [Retrieval Augmentation for Commonsense Reasoning: A Unified Approach](https://aclanthology.org/2022.emnlp-main.294/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
99. [SPoT: Better Frozen Model Adaptation through Soft Prompt Transfer](https://aclanthology.org/2022.acl-long.346/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
100. [Specializing Pre-trained Language Models for Better Relational Reasoning via Network Pruning](https://aclanthology.org/2022.findings-naacl.169/) *— 2022 · Findings of the Association for Computational Linguistics: NAACL 2022*

Pages: [1](https://md.research.elamur.ai/free/en/papers/bhagavatula-2019-abductive-commonsense-reasoning/cited-by/) · **2** · [3](https://md.research.elamur.ai/free/en/papers/bhagavatula-2019-abductive-commonsense-reasoning/cited-by/page/3/)