Papers by Yanming Zhu

2 papers
Causal-Audit: Explicit and Auditable Graph-based Reasoning via Target-Aware Causal Chain Construction (2026.findings-acl)

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Challenge: Existing LLM-based methods rely on implicit language-level reasoning, resulting in opaque causal assumptions and fragile predictions.
Approach: They propose an explicit and auditable causal reasoning framework for context-free intervention-based question answering that uses four modular stages rather than implicit end-to-end prediction.
Outcome: The proposed framework outperforms existing LLM-based methods while providing interpretable and auditable causal reasoning traces.
Intention Knowledge Graph Construction for User Intention Relation Modeling (2026.eacl-long)

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Challenge: Existing knowledge graphs focus on connecting intentions but lacks the ability to model the relationships between different intentions.
Approach: They propose a framework to automatically generate an intention knowledge graph, capturing connections between user intentions.
Outcome: The proposed model outperforms state-of-the-art methods and shows its utility.

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