Papers by Hengliang Luo

1 papers
Open-Set Living Need Prediction with Large Language Models (2025.findings-acl)

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Challenge: Existing approaches to living need prediction treat it as a closed-set classification problem, severely limiting their ability to capture diversity and complexity of living needs.
Approach: They propose a system leveraging large language models for unrestricted need prediction that leverages Maslow's hierarchy of needs to align predictions with human living needs.
Outcome: The proposed system outperforms closed-set approaches on need-based life service recall by an average of 19.37% on real-world datasets.

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