Papers by Yongkang Huang

4 papers
MAGI: Multi-Agent Guided Interview for Psychiatric Assessment (2025.findings-acl)

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Challenge: Existing large language models (LLMs) do not align with psychiatric diagnostic protocols.
Approach: They propose a framework that transforms the Mini International Neuropsychiatric Interview into automatic computational workflows through coordinated multi-agent collaboration.
Outcome: The proposed framework transforms the gold-standard Mini International Neuropsychiatric Interview (MINI) into automatic computational workflows through coordinated multi-agent collaboration.
RiTeK: A Dataset for Large Language Models Complex Reasoning over Textual Knowledge Graphs in Medicine (2026.findings-acl)

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Challenge: Existing methods for retrieving medical textual knowledge Graphs struggle to perform well, a study finds . existing methods struggle to provide accurate answers to complex questions, he says .
Approach: They synthesize user queries integrating diverse topological structures, relational information, and complex textual descriptions.
Outcome: a new dataset for medical textual knowledge graphs shows that existing methods struggle to perform well . main bottlenecks lie in the scarcity of existing medical TKGs and the limited expressiveness of their topological structures .
CharacterGLM: Customizing Social Characters with Large Language Models (2024.emnlp-industry)

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Challenge: Character-based dialogue systems (CharacterDial) allow users to customize social characters for social interactions.
Approach: They will collect a large-scale Chinese corpus of characters with diverse categories and behaviors and develop CharacterGLM models to address these challenges.
Outcome: Experiments show that CharacterGLM outperforms most popular open- and closed-source LLMs and performs comparable to GPT-4.
Crisp: Cognitive Restructuring of Negative Thoughts through Multi-turn Supportive Dialogues (2025.emnlp-main)

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Challenge: Existing approaches to cognitive restructuring (CR) are limited by entrenched cognitive distortions, emotional resistance, and individual differences.
Approach: They propose a framework that structures CR as theory-grounded multi-stage multi-turn dialogue and a multi-channel loop mechanism to account for diverse individual distortions.
Outcome: The proposed framework integrates supportive strategies for emotional management and a multi-channel loop mechanism to account for diverse individual distortions.

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