152 citations · 158 across the 16 of their papers we have counts for
16 papers
Multi-Granularity Sentiment Integration for LLM-Based Multimodal Sentiment Analysis
Shanshan Lin, Yuesheng Wu, Chao Chen +4
Multimodal sentiment analysis (MSA) aims to predict sentiment polarity and intensity from heterogeneous inputs such as text, audio, and vision. While large language models (LLMs) o…
Towards Faithful Sentimental Image Captioning via Evidence-Aware Multi-Agent Reasoning
Tiecheng Cai, Zexian Yang, Chao Chen +2
Sentimental Image Captioning (SIC) requires balancing emotional expression with visual fidelity. Existing methods often struggle with this trade-off, leading to hallucinations due…
Constrained Paraphrase Consistency for LLM Hallucination Detection
Shanshan Lin, Dongsheng Hong, Sibo Ju +3
Large language models (LLMs) can generate factually inconsistent claims, motivating accurate and scalable hallucination detectors. Prior work largely enlarges training sets via syn…
Cross Paraphrastic Invariance Learning for Hallucination Detection
Shanshan Lin, Dongsheng Hong, Sibo Ju +3
Large language models (LLMs) frequently generate hallucinations, which are unsupported by a source document. To avoid costly LLM-as-evaluator pipelines and the heavy annotation dem…
BAED: a New Paradigm for Few-shot Graph Learning with Explanation in the Loop
Chao Chen, Xujia Li, Dongsheng Hong +4
The challenges of training and inference in few-shot environments persist in the area of graph representation learning. The quality and quantity of labels are often insufficient du…
Explanation-Guided Adversarial Training for Robust and Interpretable Models
Chao Chen, Yanhui Chen, Shanshan Lin +4
Deep neural networks (DNNs) have achieved remarkable performance in many tasks, yet they often behave as opaque black boxes. Explanation-guided learning (EGL) methods steer DNNs us…
Learning How to Remember: A Meta-Cognitive Management Method for Structured and Transferable Agent Memory
Sirui Liang, Pengfei Cao, Jian Zhao +4
Large language model (LLM) agents increasingly rely on accumulated memory to solve long-horizon decision-making tasks. However, most existing approaches store memory in fixed repre…
Fine-Grained Emotion Recognition via In-Context Learning
Zhaochun Ren, Zhou Yang, Chenglong Ye +4
Fine-grained emotion recognition aims to identify the emotional type in queries through reasoning and decision-making processes, playing a crucial role in various systems. Recent m…
Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task
Nataliya Kosmyna, Eugene Hauptmann, Ye Tong Yuan +5
This study explores the neural and behavioral consequences of LLM-assisted essay writing. Participants were divided into three groups: LLM, Search Engine, and Brain-only (no tools)…
Exploring Information Processing in Large Language Models: Insights from Information Bottleneck Theory
Zhou Yang, Zhengyu Qi, Zhaochun Ren +4
Large Language Models (LLMs) have demonstrated remarkable performance across a wide range of tasks by understanding input information and predicting corresponding outputs. However,…
E-ICL: Enhancing Fine-Grained Emotion Recognition through the Lens of Prototype Theory
Zhaochun Ren, Zhou Yang, Chenglong Ye +6
In-context learning (ICL) achieves remarkable performance in various domains such as knowledge acquisition, commonsense reasoning, and semantic understanding. However, its performa…
Uncertainty Quantification on Graph Learning: A Survey
Chao Chen, Chenghua Guo, Rui Xu +6
Graphical models have demonstrated their exceptional capabilities across numerous applications. However, their performance, confidence, and trustworthiness are often limited by the…
CTSM: Combining Trait and State Emotions for Empathetic Response Model
Wang Yufeng, Chen Chao, Yang Zhou +2
Empathetic response generation endeavors to empower dialogue systems to perceive speakers' emotions and generate empathetic responses accordingly. Psychological research demonstrat…
Exploiting Emotion-Semantic Correlations for Empathetic Response Generation
Zhou Yang, Zhaochun Ren, Yufeng Wang +7
Empathetic response generation aims to generate empathetic responses by understanding the speaker's emotional feelings from the language of dialogue. Recent methods capture emotion…
Enhancing Empathetic Response Generation by Augmenting LLMs with Small-scale Empathetic Models
Zhou Yang, Zhaochun Ren, Wang Yufeng +4
Empathetic response generation is increasingly significant in AI, necessitating nuanced emotional and cognitive understanding coupled with articulate response expression. Current l…
An Iterative Associative Memory Model for Empathetic Response Generation
Zhou Yang, Zhaochun Ren, Yufeng Wang +4
Empathetic response generation aims to comprehend the cognitive and emotional states in dialogue utterances and generate proper responses. Psychological theories posit that compreh…