Papers by Hogun Park
Improving Multi-hop Logical Reasoning in Knowledge Graphs with Context-Aware Query Representation Learning (2024.findings-acl)
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| Challenge: | Existing methods rely on linear sequential operations to solve First-Order Logic queries. |
| Approach: | They propose a model-agnostic approach that fully integrates the context of the query graph. |
| Outcome: | The proposed method improves performance on two datasets by 19.5%. |
Enhancing Complex Reasoning in Knowledge Graph Question Answering through Query Graph Approximation (2025.findings-acl)
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| Challenge: | Existing knowledge-grounded question answering frameworks lack essential triplets related to the questions . Existing approaches to knowledge-based QA are incomplete in the context of KGs . |
| Approach: | They propose a framework to provide answers to structured queries by leveraging Knowledge Graphs. |
| Outcome: | The proposed framework outperforms existing methods on QA tasks where KGs are incomplete . the framework is based on a set of data from a dataset of QA questions . |
Large Language Models Are Better Logical Fallacy Reasoners with Counterargument, Explanation, and Goal-Aware Prompt Formulation (2025.findings-naacl)
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| Challenge: | Recent large language models have demonstrated improved processing of complex language, but detecting logical fallacies remains a challenge. |
| Approach: | They propose a prompt formulation approach for logical fallacy detection that integrates contextual information into input text and queries for validity within the argument’s context. |
| Outcome: | The proposed approach improves over state-of-the-art models by 0.57 in F1-scores and 0.45 in fine-tuned models. |