32 citations · 63 across the 64 of their papers we have counts for
16 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…
ChatZero:Zero-shot Cross-Lingual Dialogue Generation via Pseudo-Target Language
Yongkang Liu, Feng Shi, Daling Wang +2
Although large language models(LLMs) show amazing capabilities, among various exciting applications discovered for LLMs fall short in other low-resource languages. Besides, most ex…
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…
A Unified Data Augmentation Framework for Low-Resource Multi-Domain Dialogue Generation
Yongkang Liu, Ercong Nie, Shi Feng +5
Current state-of-the-art dialogue systems heavily rely on extensive training datasets. However, challenges arise in domains where domain-specific training datasets are insufficient…
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…
Is Mamba Effective for Time Series Forecasting?
Zihan Wang, Fanheng Kong, Shi Feng +5
In the realm of time series forecasting (TSF), it is imperative for models to adeptly discern and distill hidden patterns within historical time series data to forecast future stat…
STICKERCONV: Generating Multimodal Empathetic Responses from Scratch
Yiqun Zhang, Fanheng Kong, Peidong Wang +6
Stickers, while widely recognized for enhancing empathetic communication in online interactions, remain underexplored in current empathetic dialogue research, notably due to the ch…
LangGPT: Rethinking Structured Reusable Prompt Design Framework for LLMs from the Programming Language
Ming Wang, Yuanzhong Liu, Xiaoyu Liang +11
LLMs have demonstrated commendable performance across diverse domains. Nevertheless, formulating high-quality prompts to instruct LLMs proficiently poses a challenge for non-AI exp…
HiFT: A Hierarchical Full Parameter Fine-Tuning Strategy
Yongkang Liu, Yiqun Zhang, Qian Li +5
Full-parameter fine-tuning has become the go-to choice for adapting language models (LMs) to downstream tasks due to its excellent performance. As LMs grow in size, fine-tuning the…