32 citations · 48 across the 47 of their papers we have counts for
11 papers · 1 filter
PsyDraw: A Multi-Agent Multimodal System for Mental Health Screening in Left-Behind Children
Yiqun Zhang, Xiaocui Yang, Xiaobai Li +5
Left-behind children (LBCs), numbering over 66 million in China, face severe mental health challenges due to parental migration for work. Early screening and identification of at-r…
TOOL-ED: Enhancing Empathetic Response Generation with the Tool Calling Capability of LLM
Huiying Cao, Yiqun Zhang, Shi Feng +3
Empathetic conversation is a crucial characteristic in daily conversations between individuals. Nowadays, Large Language models (LLMs) have shown outstanding performance in generat…
Muse: A Multimodal Conversational Recommendation Dataset with Scenario-Grounded User Profiles
Zihan Wang, Xiaocui Yang, Yongkang Liu +3
Current conversational recommendation systems focus predominantly on text. However, real-world recommendation settings are generally multimodal, causing a significant gap between e…
Language Models as Continuous Self-Evolving Data Engineers
Peidong Wang, Ming Wang, Zhiming Ma +5
Large Language Models (LLMs) have demonstrated remarkable capabilities on various tasks, while the further evolvement is limited to the lack of high-quality training data. In addit…
Generative Emotion Cause Explanation in Multimodal Conversations
Lin Wang, Xiaocui Yang, Shi Feng +3
Multimodal conversation, a crucial form of human communication, carries rich emotional content, making the exploration of the causes of emotions within it a research endeavor of si…
Minstrel: Structural Prompt Generation with Multi-Agents Coordination for Non-AI Experts
Ming Wang, Yuanzhong Liu, Xiaoyu Liang +8
LLMs have demonstrated commendable performance across diverse domains. Nevertheless, formulating high-quality prompts to assist them in their work poses a challenge for non-AI expe…
Hierarchical Retrieval-Augmented Generation Model with Rethink for Multi-hop Question Answering
Xiaoming Zhang, Ming Wang, Xiaocui Yang +3
Multi-hop Question Answering (QA) necessitates complex reasoning by integrating multiple pieces of information to resolve intricate questions. However, existing QA systems encounte…
Can LLMs Beat Humans in Debating? A Dynamic Multi-agent Framework for Competitive Debate
Yiqun Zhang, Xiaocui Yang, Shi Feng +3
Competitive debate is a complex task of computational argumentation. Large Language Models (LLMs) suffer from hallucinations and lack competitiveness in this field. To address thes…
Affective Computing in the Era of Large Language Models: A Survey from the NLP Perspective
Yiqun Zhang, Xiaocui Yang, Xingle Xu +8
Affective Computing (AC) integrates computer science, psychology, and cognitive science to enable machines to recognize, interpret, and simulate human emotions across domains such…
MM-InstructEval: Zero-Shot Evaluation of (Multimodal) Large Language Models on Multimodal Reasoning Tasks
Xiaocui Yang, Wenfang Wu, Shi Feng +7
The emergence of multimodal large language models (MLLMs) has triggered extensive research in model evaluation. While existing evaluation studies primarily focus on unimodal (visio…
Consistency Guided Knowledge Retrieval and Denoising in LLMs for Zero-shot Document-level Relation Triplet Extraction
Qi Sun, Kun Huang, Xiaocui Yang +3
Document-level Relation Triplet Extraction (DocRTE) is a fundamental task in information systems that aims to simultaneously extract entities with semantic relations from a documen…