32 citations · 54 across the 18 of their papers we have counts for
19 papers
CR-Seg: Attention-Guided and CoT-Enhanced Coarse-to-Refined Reasoning Segmentation
Yifan Cao, Xiaocui Yang, Faxian Wan +3
Reasoning segmentation aims to segment target objects described by complex language through joint visual-textual reasoning. Existing methods typically rely on either learned semant…
Pixel-Level Reasoning Segmentation via Multi-turn Conversations
Dexian Cai, Xiaocui Yang, Yongkang Liu +4
Existing visual perception systems focus on region-level segmentation in single-turn dialogues, relying on complex and explicit query instructions. Such systems cannot reason at th…
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…
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…
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…
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…
MM-BigBench: Evaluating Multimodal Models on Multimodal Content Comprehension Tasks
Xiaocui Yang, Wenfang Wu, Shi Feng +7
The popularity of multimodal large language models (MLLMs) has triggered a recent surge in research efforts dedicated to evaluating these models. Nevertheless, existing evaluation…
T-COL: Generating Counterfactual Explanations for General User Preferences on Variable Machine Learning Systems
Ming Wang, Daling Wang, Wenfang Wu +2
To address the interpretability challenge in machine learning (ML) systems, counterfactual explanations (CEs) have emerged as a promising solution. CEs are unique as they provide w…
RoCar: A Relationship Network-based Evaluation Method for Large Language Models
Ming Wang, Wenfang Wu, Chongyun Gao +3
Large language models (LLMs) have received increasing attention. However, due to the complexity of its capabilities, how to rationally evaluate the capabilities of LLMs is still a…
Few-shot Joint Multimodal Aspect-Sentiment Analysis Based on Generative Multimodal Prompt
Xiaocui Yang, Shi Feng, Daling Wang +5
We have witnessed the rapid proliferation of multimodal data on numerous social media platforms. Conventional studies typically require massive labeled data to train models for Mul…
Evaluate What You Can't Evaluate: Unassessable Quality for Generated Response
Yongkang Liu, Shi Feng, Daling Wang +2
LLMs (large language models) such as ChatGPT have shown remarkable language understanding and generation capabilities. Although reference-free evaluators based on LLMs show better…
PVGRU: Generating Diverse and Relevant Dialogue Responses via Pseudo-Variational Mechanism
Yongkang Liu, Shi Feng, Daling Wang +2
We investigate response generation for multi-turn dialogue in generative-based chatbots. Existing generative models based on RNNs (Recurrent Neural Networks) usually employ the las…
Alleviating Sparsity of Open Knowledge Graphs with Ternary Contrastive Learning
Qian Li, Shafiq Joty, Daling Wang +2
Sparsity of formal knowledge and roughness of non-ontological construction make sparsity problem particularly prominent in Open Knowledge Graphs (OpenKGs). Due to sparse links, lea…
Few-shot Multimodal Sentiment Analysis based on Multimodal Probabilistic Fusion Prompts
Xiaocui Yang, Shi Feng, Daling Wang +2
Multimodal sentiment analysis has gained significant attention due to the proliferation of multimodal content on social media. However, existing studies in this area rely heavily o…
A Graph Reasoning Network for Multi-turn Response Selection via Customized Pre-training
Yongkang Liu, Shi Feng, Daling Wang +3
We investigate response selection for multi-turn conversation in retrieval-based chatbots. Existing studies pay more attention to the matching between utterances and responses by c…