58 citations · 62 across the 12 of their papers we have counts for
7 papers · 1 filter
Listwise Preference Optimization with Element-wise Confusions for Aspect Sentiment Quad Prediction
Wenna Lai, Haoran Xie, Guandong Xu +2
Aspect sentiment quad prediction (ASQP) is inherently challenging to predict a structured quadruple with four core sentiment elements, including aspect term (a), aspect category (c…
Emotion-Enhanced Multi-Task Learning with LLMs for Aspect Category Sentiment Analysis
Yaping Chai, Haoran Xie, Joe S. Qin
Aspect category sentiment analysis (ACSA) has achieved remarkable progress with large language models (LLMs), yet existing approaches primarily emphasize sentiment polarity while o…
Calibration and Transformation-Free Weight-Only LLMs Quantization via Dynamic Grouping
Xinzhe Zheng, Zhen-Qun Yang, Zishan Liu +4
Large Language Models (LLMs) deliver strong performance but are difficult to deploy under tight memory and compute constraints. Low-bit post-training quantization (PTQ) is a promis…
Semantic-preserved Augmentation with Confidence-weighted Fine-tuning for Aspect Category Sentiment Analysis
Yaping Chai, Haoran Xie, Joe S. Qin
Large language model (LLM) is an effective approach to addressing data scarcity in low-resource scenarios. Recent existing research designs hand-crafted prompts to guide LLM for da…
When LLMs Team Up: The Emergence of Collaborative Affective Computing
Wenna Lai, Haoran Xie, Guandong Xu +2
Affective Computing (AC) is essential in bridging the gap between human emotional experiences and machine understanding. Traditionally, AC tasks in natural language processing (NLP…
Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective
Xiaorui Ma, Haoran Xie, S. Joe Qin
The integration of vision-language modalities has been a significant focus in multimodal learning, traditionally relying on Vision-Language Pretrained Models. However, with the adv…
CondAmbigQA: A Benchmark and Dataset for Conditional Ambiguous Question Answering
Zongxi Li, Yang Li, Haoran Xie +1
Users often assume that large language models (LLMs) share their cognitive alignment of context and intent, leading them to omit critical information in question-answering (QA) and…