mlsanityData to 7 Oct 2026

COPA-SSE: Semi-structured Explanations for Commonsense Reasoning

arXiv 2201.06777 ↗First posted 18 Jan 2022cs.CLcited by 8

On the map: LLM reasoning → Reasoning benchmarks and evaluation

Abstract (arXiv)

We present Semi-Structured Explanations for COPA (COPA-SSE), a new crowdsourced dataset of 9,747 semi-structured, English common sense explanations for Choice of Plausible Alternatives (COPA) questions. The explanations are formatted as a set of triple-like common sense statements with ConceptNet relations but freely written concepts. This semi-structured format strikes a balance between the high quality but low coverage of structured data and the lower quality but high coverage of free-form crowdsourcing. Each explanation also includes a set of human-given quality ratings. With their familiar format, the explanations are geared towards commonsense reasoners operating on knowledge graphs and serve as a starting point for ongoing work on improving such systems. The dataset is available at https://github.com/a-brassard/copa-sse.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning. Builds on 15 of its 21 references that are in the map. First posted on arXiv 18 Jan 2022.

Builds on

15 of 21 references.

References · 15

  1. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  2. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  3. 2018CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledgecited by 1,793
  4. 2017Explanation in Artificial Intelligence: Insights from the Social Sciencescited by 1,201
  5. 2016ConceptNet 5.5: An Open Multilingual Graph of General Knowledgecited by 1,065
  6. 2018Annotation Artifacts in Natural Language Inference Datacited by 656
  7. 2021QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answeringcited by 386
  8. 2019Explain Yourself! Leveraging Language Models for Commonsense Reasoningcited by 367
  9. 2020COMET-ATOMIC 2020: On Symbolic and Neural Commonsense Knowledge Graphscited by 254
  10. 2019KagNet: Knowledge-Aware Graph Networks for Commonsense Reasoningcited by 216
  11. 2021What Will it Take to Fix Benchmarking in Natural Language Understanding?cited by 200
  12. 2021QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehensioncited by 91
3 more
  1. 2021ExplaGraphs: An Explanation Graph Generation Task for Structured Commonsense Reasoningcited by 58
  2. 2019When Choosing Plausible Alternatives, Clever Hans can be Clevercited by 39
  3. 2018Quality Control in Crowdsourcing: A Survey of Quality Attributes, Assessment Techniques and Assurance Actionscited by 16

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 3; 2025: 1; 2026: 1.

8 papers.

  1. 2024Tokenization Is More Than Compressioncited by 87
  2. 2024On Evaluating Explanation Utility for Human-AI Decision Making in NLPcited by 9
  3. 2024JourneyBench: A Challenging One-Stop Vision-Language Understanding Benchmark of Generated Imagescited by 5
  4. 2025Improving the OOD Performance of Closed-Source LLMs on NLI Through Strategic Data Selectioncited by 4
  5. 2023Gradient Ascent Post-training Enhances Language Model Generalizationcited by 3
  6. 2023Text-To-KG Alignment: Comparing Current Methods on Classification Taskscited by 2
  7. 2023Reward Engineering for Generating Semi-structured Explanationcited by 0
  8. 2026CommonWhy: A Dataset for Evaluating Entity-Based Causal Commonsense Reasoning in Large Language Modelscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2023UNcommonsense Reasoning: Abductive Reasoning about Uncommon Situations
  2. 2025Towards Quantifying Commonsense Reasoning with Mechanistic Insights
  3. 2024From Data to Commonsense Reasoning: The Use of Large Language Models for Explainable AI
  4. 2024Plausibly Problematic Questions in Multiple-Choice Benchmarks for Commonsense Reasoning
  5. 2022A Theoretically Grounded Benchmark for Evaluating Machine Commonsense
  6. 2024What Really is Commonsense Knowledge?
  7. 2022Possible Stories: Evaluating Situated Commonsense Reasoning under Multiple Possible Scenarios
  8. 2025ReTraceQA: Evaluating Reasoning Traces of Small Language Models in Commonsense Question Answering