Papers by Jiaqiang Wu

2 papers
Integrating Visual Modalities with Large Language Models for Mental Health Support (2025.coling-main)

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Challenge: Existing work of mental health support primarily utilizes unimodal textual data and fails to understand and respond to users’ emotional states comprehensively.
Approach: They propose a framework that integrates multimodal inputs and counseling strategies to enhance the performance of Large Language Models (LLMs) This approach allows LLMs to generate more nuanced and supportive responses.
Outcome: The proposed framework outperforms existing models and delivers more empathetic, coherent, and contextually relevant mental health support responses.
From Traits to Empathy: Personality-Aware Multimodal Empathetic Response Generation (2025.coling-main)

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Challenge: Existing approaches focus on acquiring affective and cognitive knowledge from text, but neglect the unique personality traits of individuals and the inherently multimodal nature of human face-to-face conversation.
Approach: They propose a multimodal dialogue system that generates empathetic responses from a perspective that considers the personality traits of users.
Outcome: The proposed system generates empathetic responses from a multimodal perspective and analyzes multimodal data to understand the user’s emotional state and situation.

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