Papers by Zhiguang Gao

2 papers
Depression Detection on Social Media with Large Language Models (2025.emnlp-industry)

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Challenge: Existing methods for analyzing social media data lack a systematic integration of medical knowledge, causing a critical treatment gap.
Approach: They propose a framework that leverages Large Language Models to integrate medical knowledge into social media data.
Outcome: The proposed framework can be used to distinguish depression from transient mood changes.
Factual Relation Discrimination for Factuality-oriented Abstractive Summarization (2023.findings-emnlp)

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Challenge: Existing factuality-oriented abstractive summarization models only consider the integration of factual information and ignore the causes of factuual errors.
Approach: They propose a factuality-oriented abstractive summarization model that can identify the causes of factual errors.
Outcome: The proposed model outperforms state-of-the-art models in factual metrics.

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