mlsanityData to 7 Oct 2026

Does It Make Sense? And Why? A Pilot Study for Sense Making and Explanation

arXiv 1906.00363 ↗First posted 2 Jun 2019cs.AI cs.CLcited by 43

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Abstract (arXiv)

Introducing common sense to natural language understanding systems has received increasing research attention. It remains a fundamental question on how to evaluate whether a system has a sense making capability. Existing benchmarks measures commonsense knowledge indirectly and without explanation. In this paper, we release a benchmark to directly test whether a system can differentiate natural language statements that make sense from those that do not make sense. In addition, a system is asked to identify the most crucial reason why a statement does not make sense. We evaluate models trained over large-scale language modeling tasks as well as human performance, showing that there are different challenges for system sense making.

Key facts. Cited by 43 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Jun 2019.

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Citing papers per year: 2021: 10; 2022: 8; 2023: 15; 2024: 7; 2025: 2; 2026: 1.

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  2. 2023PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimizationcited by 311
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  9. 2021Few-Shot Self-Rationalization with Natural Language Promptscited by 83
  10. 2021When Can Models Learn From Explanations? A Formal Framework for Understanding the Roles of Explanation Datacited by 75
  11. 2024Can LLMs Learn from Previous Mistakes? Investigating LLMs' Errors to Boost for Reasoningcited by 69
  12. 2021Can Generative Pre-trained Language Models Serve as Knowledge Bases for Closed-book QA?cited by 62
31 more
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  2. 2021ExplaGraphs: An Explanation Graph Generation Task for Structured Commonsense Reasoningcited by 58
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  4. 2022Going Beyond XAI: A Systematic Survey for Explanation-Guided Learningcited by 35
  5. 2021Local Interpretations for Explainable Natural Language Processing: A Surveycited by 31
  6. 2023Assessing Hidden Risks of LLMs: An Empirical Study on Robustness, Consistency, and Credibilitycited by 28
  7. 2021Knowledge-Grounded Self-Rationalization via Extractive and Natural Language Explanationscited by 24
  8. 2023Explanation-based Finetuning Makes Models More Robust to Spurious Cuescited by 24
  9. 2022Diversifying Content Generation for Commonsense Reasoning with Mixture of Knowledge Graph Expertscited by 19
  10. 2023Can Language Models Understand Physical Concepts?cited by 19
  11. 2021Exploring Generalization Ability of Pretrained Language Models on Arithmetic and Logical Reasoningcited by 17
  12. 2022How Conservative are Language Models? Adapting to the Introduction of Gender-Neutral Pronounscited by 16
  13. 2023Abductive Commonsense Reasoning Exploiting Mutually Exclusive Explanationscited by 11
  14. 2021Constrained Text Generation with Global Guidance -- Case Study on CommonGencited by 10
  15. 2023Fully Authentic Visual Question Answering Dataset from Online Communitiescited by 9
  16. 2024On Evaluating Explanation Utility for Human-AI Decision Making in NLPcited by 9
  17. 2023ZARA: Improving Few-Shot Self-Rationalization for Small Language Modelscited by 8
  18. 2023CRoW: Benchmarking Commonsense Reasoning in Real-World Taskscited by 8
  19. 2022Unsupervised Explanation Generation via Correct Instantiationscited by 5
  20. 2025The Box is in the Pen: Evaluating Commonsense Reasoning in Neural Machine Translationcited by 5
  21. 2021Learning to Learn to be Right for the Right Reasonscited by 4
  22. 2022ElitePLM: An Empirical Study on General Language Ability Evaluation of Pretrained Language Modelscited by 4
  23. 2022Explanation Regeneration via Information Bottleneckcited by 4
  24. 2023SPARSEFIT: Few-shot Prompting with Sparse Fine-tuning for Jointly Generating Predictions and Natural Language Explanationscited by 2
  25. 2024Leveraging Open Knowledge for Advancing Task Expertise in Large Language Modelscited by 2
  26. 2024Rule or Story, Which is a Better Commonsense Expression for Talking with Large Language Models?cited by 1
  27. 2025Self-Rationalization in the Wild: A Large Scale Out-of-Distribution Evaluation on NLI-related taskscited by 1
  28. 2023Neural Keyphrase Generation: Analysis and Evaluationcited by 0
  29. 2024Large Language Models Know What Makes Exemplary Contextscited by 0
  30. 2024Contextual Breach: Assessing the Robustness of Transformer-based QA Modelscited by 0
  31. 2026CAREF: Calibration-Aware Regularization for Explanation Faithfulness Without Rationale Supervisioncited by 0