Papers by Wang Yuanlong
Enhancing Event Causality Identification with LLM Knowledge and Concept-Level Event Relations (2025.coling-main)
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| Challenge: | Existing methods to identify causal relationships between events often overlook the dependencies between similar events. |
| Approach: | They propose an ECI method enhanced by LLM Knowledge and Concept-Level Event Relations (LKCER) the method constructs a conceptual-level heterogeneous event graph by leveraging local contextual information of related event mentions. |
| Outcome: | The proposed method outperforms previous state-of-the-art methods on both benchmarks, EventStoryLine and Causal-TimeBank. |
Leibniz: Theory-of-Mind Driven Neuro-Symbolic Logical Reasoning via Multi-Agent Collaboration (2026.acl-long)
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| Challenge: | Existing methods for logical reasoning with large language models suffer from insufficient rule semantic grounding and weak rule application mechanisms. |
| Approach: | They propose a theory-of-mind driven neuro-symbolic reasoning framework that integrates natural language and symbolic representations throughout the reasoning process. |
| Outcome: | The proposed model surpasses state-of-the-art models in reasoning accuracy and flexibility. |
Improving Sequential Model Editing with Fact Retrieval (2023.findings-emnlp)
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| Challenge: | Existing methods to fix erroneous knowledge in Pre-trained Language models experience a performance decline when the number of edits increases. |
| Approach: | They propose a framework that leverages factual information to enhance editing generalization and guide the identification of edits by retrieving related facts from the fact-patch memory. |
| Outcome: | The proposed framework can improve model generalization and accuracy even with thousands of edits. |