This work proposes a novel Rating-Aware Argument Generation framework designed to produce persuasive arguments aligned with fine-grained rating intensities, and demonstrates that this method is effective in improving rating alignment and mitigating rating drift.
This paper presents a framework that preserves semantics in LLM-based opinion summarization while minimizing token usage and computational cost and demonstrates that this method significantly reduces token usage and computational cost while consistently outperforming traditional AI-based and standard LLM summarization...
MMRP is proposed, an end-to-end multimodal multitask framework for jointly modeling ACSA and ORP from image-text reviews and demonstrates the effectiveness of jointly leveraging multimodal information for aspect-level and overall rating prediction in user reviews.
Xiaoning Wang, Rui Li, Yuxuan Guo et al.· Knowledge and Information Sy...· 0 citations
This work proposes the SENSE model, a confidence-aware soft sentiment assignment and a sentiment exchange mechanism to quantify fine-grained preferences and explicitly model sentiment interaction, and employs a confidence-aware soft sentiment assignment and a sentiment exchange mechanism to quantify fine-grained prefer...
Ying-Jie Chen, Xiang Li, Dong Chen et al.· Proceedings of the Thirty-Fi...· 0 citations
Obvious performance discrepancies exist across LLMs; high-quality annotations significantly boost downstream classification accuracy, which highlights the importance of selecting competent LLMs for sentiment labeling tasks.
Wenjing Pi, Changxian He· Computers and artificial int...· 0 citations
This work proposes Snap2Review, a novel framework that harmonizes cross-modal retrieval with fine-grained preference learning, and introduces Tri-DPO, which employs a stratified negative sampling strategy with varying difficulty levels to force the model to discern fine-grained user preferences.
Honggyan Xu, Lei Zou, De-Xiang Zhao et al.· Proceedings of the Thirty-Fi...· 0 citations
This paper introduces a unified framework for explainable rating prediction and sentiment-segmented summarization, which employs a neural network for accurate rating prediction and a Retrieval-Augmented Generation module for interpretable justifications of predicted ratings.
Prince Pandey, Jyoti Prakash Singh· SN Computer Science· 0 citations
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