32 citations · 48 across the 47 of their papers we have counts for
10 papers · 1 filter
Resource-Limited Joint Multimodal Sentiment Reasoning and Classification via Chain-of-Thought Enhancement and Distillation
Haonan Shangguan, Xiaocui Yang, Shi Feng +3
Current approaches for Multimodal Sentiment Analysis (MSA) primarily leverage the knowledge and reasoning capabilities of parameter-heavy (Multimodal) LLMs for classification, over…
MoLAN: A Unified Modality-Aware Noise Dynamic Editing Framework for Multimodal Sentiment Analysis
Xingle Xu, Yongkang Liu, Dexian Cai +4
Multimodal Sentiment Analysis aims to integrate information from various modalities, such as audio, visual, and text, to make complementary predictions. However, it often struggles…
MEKiT: Multi-source Heterogeneous Knowledge Injection Method via Instruction Tuning for Emotion-Cause Pair Extraction
Shiyi Mu, Yongkang Liu, Shi Feng +3
Although large language models (LLMs) excel in text comprehension and generation, their performance on the Emotion-Cause Pair Extraction (ECPE) task, which requires reasoning abili…
AnnaAgent: Dynamic Evolution Agent System with Multi-Session Memory for Realistic Seeker Simulation
Ming Wang, Peidong Wang, Lin Wu +6
Constrained by the cost and ethical concerns of involving real seekers in AI-driven mental health, researchers develop LLM-based conversational agents (CAs) with tailored configura…
Why Do More Experts Fail? A Theoretical Analysis of Model Merging
Zijing Wang, Xingle Xu, Yongkang Liu +6
Model merging dramatically reduces storage and computational resources by combining multiple expert models into a single multi-task model. Although recent model merging methods hav…
Modality Curation: Building Universal Embeddings for Advanced Multimodal Information Retrieval
Fanheng Kong, Jingyuan Zhang, Yahui Liu +8
Multimodal information retrieval (MIR) faces inherent challenges due to the heterogeneity of data sources and the complexity of cross-modal alignment. While previous studies have i…
Enhancing LLM-based Recommendation through Semantic-Aligned Collaborative Knowledge
Zihan Wang, Jinghao Lin, Xiaocui Yang +4
Large Language Models (LLMs) demonstrate remarkable capabilities in leveraging comprehensive world knowledge and sophisticated reasoning mechanisms for recommendation tasks. Howeve…
Nature-Inspired Population-Based Evolution of Large Language Models
Yiqun Zhang, Peng Ye, Xiaocui Yang +5
Evolution, the engine behind the survival and growth of life on Earth, operates through the population-based process of reproduction. Inspired by this principle, this paper formall…
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…
HIPPO: Enhancing the Table Understanding Capability of LLMs through Hybrid-Modal Preference Optimization
Haolan Wang, Zhenghao Liu, Xinze Li +7
Tabular data contains rich structural semantics and plays a crucial role in organizing and manipulating information. Recent methods employ Multi-modal Large Language Models (MLLMs)…