mlsanityData to 7 Oct 2026

The Odyssey of Commonsense Causality: From Foundational Benchmarks to Cutting-Edge Reasoning

arXiv 2406.19307 ↗First posted 27 Jun 2024cs.CLcited by 5

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Abstract (arXiv)

Understanding commonsense causality is a unique mark of intelligence for humans. It helps people understand the principles of the real world better and benefits the decision-making process related to causation. For instance, commonsense causality is crucial in judging whether a defendant's action causes the plaintiff's loss in determining legal liability. Despite its significance, a systematic exploration of this topic is notably lacking. Our comprehensive survey bridges this gap by focusing on taxonomies, benchmarks, acquisition methods, qualitative reasoning, and quantitative measurements in commonsense causality, synthesizing insights from over 200 representative articles. Our work aims to provide a systematic overview, update scholars on recent advancements, provide a pragmatic guide for beginners, and highlight promising future research directions in this vital field.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 64 of its 220 references that are in the map. First posted on arXiv 27 Jun 2024.

Builds on

64 of 220 references.

References · 64

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2020Language Models are Few-Shot Learnerscited by 32,199
  4. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  5. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  6. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  7. 2020Scaling Laws for Neural Language Modelscited by 6,411
  8. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
  9. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,304
  10. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,346
  11. 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,243
  12. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,016
38 more
  1. 2021On the Opportunities and Risks of Foundation Modelscited by 3,961
  2. 2014Sequence to Sequence Learning with Neural Networkscited by 2,974
  3. 2019XLNet: Generalized Autoregressive Pretraining for Language Understandingcited by 2,801
  4. 2019ALBERT: A Lite BERT for Self-supervised Learning of Language Representationscited by 2,772
  5. 2019ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Taskscited by 2,219
  6. 2020DeBERTa: Decoding-enhanced BERT with Disentangled Attentioncited by 2,157
  7. 2019BioBERT: a pre-trained biomedical language representation model for biomedical text miningcited by 1,935
  8. 2022LaMDA: Language Models for Dialog Applicationscited by 1,218
  9. 2020Oscar: Object-Semantics Aligned Pre-training for Vision-Language Taskscited by 1,213
  10. 2014Convolutional Neural Networks for Sentence Classificationcited by 1,124
  11. 2016ConceptNet 5.5: An Open Multilingual Graph of General Knowledgecited by 1,065
  12. 2016A Corpus and Evaluation Framework for Deeper Understanding of Commonsense Storiescited by 413
  13. 2023Causal Reasoning and Large Language Models: Opening a New Frontier for Causalitycited by 348
  14. 2020XCOPA: A Multilingual Dataset for Causal Commonsense Reasoningcited by 330
  15. 2019ERNIE: Enhanced Representation through Knowledge Integrationcited by 301
  16. 2020Social Chemistry 101: Learning to Reason about Social and Moral Normscited by 253
  17. 2022Reasoning with Language Model Prompting: A Surveycited by 251
  18. 2021Polyjuice: Generating Counterfactuals for Explaining, Evaluating, and Improving Modelscited by 219
  19. 2020A Survey on Causal Inferencecited by 202
  20. 2021Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyondcited by 177
  21. 2022SODA: Million-scale Dialogue Distillation with Social Commonsense Contextualizationcited by 166
  22. 2023Can Large Language Models Infer Causation from Correlation?cited by 142
  23. 2023CLadder: Assessing Causal Reasoning in Language Modelscited by 102
  24. 2019ASER: A Large-scale Eventuality Knowledge Graphcited by 89
  25. 2019Counterfactual Story Reasoning and Generationcited by 87
  26. 2020GLUCOSE: GeneraLized and COntextualized Story Explanationscited by 85
  27. 2018Event2Mind: Commonsense Inference on Events, Intents, and Reactionscited by 82
  28. 2020Generate Your Counterfactuals: Towards Controlled Counterfactual Generation for Textcited by 75
  29. 2019SemEval-2010 Task 8: Multi-Way Classification of Semantic Relations Between Pairs of Nominalscited by 63
  30. 2019Joint Reasoning for Temporal and Causal Relationscited by 60
  31. 2020A Tutorial on Learning With Bayesian Networkscited by 56
  32. 2020CRAFT: A Benchmark for Causal Reasoning About Forces and inTeractionscited by 44
  33. 2023Benchmarks for Automated Commonsense Reasoning: A Surveycited by 40
  34. 2022Commonsense Knowledge Reasoning and Generation with Pre-trained Language Models: A Surveycited by 38
  35. 2022DoCoGen: Domain Counterfactual Generation for Low Resource Domain Adaptationcited by 36
  36. 2014Distinguishing cause from effect using observational data: methods and benchmarkscited by 33
  37. 2021Guided Generation of Cause and Effectcited by 33
  38. 2022Long-term Causal Inference Under Persistent Confounding via Data Combinationcited by 27

50 most cited of 64 All 64 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 1; 2026: 1.

5 papers.

  1. 2024CodeGraph: Enhancing Graph Reasoning of LLMs with Codecited by 6
  2. 2024Nuance Matters: Probing Epistemic Consistency in Causal Reasoningcited by 1
  3. 2024From Correlation to Causation: Understanding Climate Change through Causal Analysis and LLM Interpretationscited by 1
  4. 2025CRAwDAD: Causal Reasoning Augmentation with Dual-Agent Debatecited by 1
  5. 2026Contextual Causality with Large Language Models: A Surveycited by 0