152 citations · 157 across the 13 of their papers we have counts for
10 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…