SemEval-2026 Task 12: Abductive Event Reasoning: Towards Real-World Event Causal Inference for Large Language Models
Not on the map. Why?
Abstract (arXiv)
Understanding why real-world events occur is important for both natural language processing and practical decision-making, yet direct-cause inference remains underexplored in evidence-rich settings. To address this gap, we organized SemEval-2026 Task 12: Abductive Event Reasoning (AER).footnoteThe task data is available at https://github.com/sooo66/semeval2026-task12-dataset.git The task asks systems to identify the most plausible direct cause of a target event from supporting evidence. We formulate AER as an evidence-grounded multiple-choice benchmark that captures key challenges of real-world causal reasoning, including distributed evidence, indirect background factors, and semantically related but non-causal distractors. The shared task attracted 122 participants and received 518 submissions. This paper presents the task formulation, dataset construction pipeline, evaluation setup, and system results. AER provides a focused benchmark for abductive reasoning over real-world events and highlights challenges for future work on causal reasoning and multi-document understanding.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 18 of its 52 references that are in the map. First posted on arXiv 23 Mar 2026.
Builds on
18 of 52 references.
References · 18
- 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,120
- 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,686
- 2025Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilitiescited by 3,655
- 2020DeBERTa: Decoding-enhanced BERT with Disentangled Attentioncited by 2,157
- 2023LongBench: A Bilingual, Multitask Benchmark for Long Context Understandingcited by 1,378
- 2024ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Toolscited by 1,096
- 2020XCOPA: A Multilingual Dataset for Causal Commonsense Reasoningcited by 330
- 2019Abductive Commonsense Reasoningcited by 300
- 2022MAVEN-ERE: A Unified Large-scale Dataset for Event Coreference, Temporal, Causal, and Subevent Relation Extractioncited by 65
- 2020Joint Entity and Relation Extraction with Set Prediction Networkscited by 54
- 2019Multi-hop Reading Comprehension across Multiple Documents by Reasoning over Heterogeneous Graphscited by 53
- 2023CRAB: Assessing the Strength of Causal Relationships Between Real-world Eventscited by 27
6 more
- 2021Improving Event Causality Identification via Self-Supervised Representation Learning on External Causal Statementcited by 21
- 2016Extracting Temporal and Causal Relations between Eventscited by 16
- 2021From Discourse to Narrative: Knowledge Projection for Event Relation Extractioncited by 14
- 2024Identifying while Learning for Document Event Causality Identificationcited by 6
- 2024Zero-Shot Cross-Lingual Document-Level Event Causality Identification with Heterogeneous Graph Contrastive Transfer Learningcited by 3
- 2026AILS-NTUA at SemEval-2026 Task 12: Graph-Based Retrieval and Reflective Prompting for Abductive Event Reasoningcited by 2
Built on by
Not cited yet.
Similar, not cited
Close in wording, no citation either way
- 2023Language Models Can Improve Event Prediction by Few-Shot Abductive Reasoning
- 2025ACCESS : A Benchmark for Abstract Causal Event Discovery and Reasoning
- 2024A Critical Review of Causal Reasoning Benchmarks for Large Language Models
- 2023CLadder: Assessing Causal Reasoning in Language Models
- 2023UNcommonsense Reasoning: Abductive Reasoning about Uncommon Situations
- 2022ROCK: Causal Inference Principles for Reasoning about Commonsense Causality
- 2026Wiring the 'Why': A Unified Taxonomy and Survey of Abductive Reasoning in LLMs
- 2026CausalFlip: A Benchmark for LLM Causal Judgment Beyond Semantic Matching