Knowledge Graph-Augmented Korean Generative Commonsense Reasoning
On the map: LLM reasoning → Reasoning benchmarks and evaluation
Abstract (arXiv)
Generative commonsense reasoning refers to the task of generating acceptable and logical assumptions about everyday situations based on commonsense understanding. By utilizing an existing dataset such as Korean CommonGen, language generation models can learn commonsense reasoning specific to the Korean language. However, language models often fail to consider the relationships between concepts and the deep knowledge inherent to concepts. To address these limitations, we propose a method to utilize the Korean knowledge graph data for text generation. Our experimental result shows that the proposed method can enhance the efficiency of Korean commonsense inference, thereby underlining the significance of employing supplementary data.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning. Builds on 6 of its 13 references that are in the map. First posted on arXiv 26 Jun 2023.
Builds on
6 of 13 references.
References · 6
- 2017Attention Is All You Needcited by 60,361
- 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,893
- 2019BERTScore: Evaluating Text Generation with BERTcited by 5,512
- 2020Multilingual Denoising Pre-training for Neural Machine Translationcited by 1,035
- 2020KG-BART: Knowledge Graph-Augmented BART for Generative Commonsense Reasoningcited by 106
- 2019How Language-Neutral is Multilingual BERT?cited by 51
Built on by
Not cited yet.
Similar, not cited
Close in wording, no citation either way
- 2022Diversifying Content Generation for Commonsense Reasoning with Mixture of Knowledge Graph Experts
- 2022Commonsense Knowledge Reasoning and Generation with Pre-trained Language Models: A Survey
- 2022Revisiting Generative Commonsense Reasoning: A Pre-Ordering Approach
- 2024Improving Diversity of Commonsense Generation by Large Language Models via In-Context Learning
- 2023SituatedGen: Incorporating Geographical and Temporal Contexts into Generative Commonsense Reasoning
- 2025Ko-PIQA: A Korean Physical Commonsense Reasoning Dataset with Cultural Context
- 2022DimonGen: Diversified Generative Commonsense Reasoning for Explaining Concept Relationships
- 2024The Mystery of Compositional Generalization in Graph-based Generative Commonsense Reasoning