4 citations · 6 across the 6 of their papers we have counts for
6 papers
Distilling Fine-grained Sentiment Understanding from Large Language Models
Yice Zhang, Guangyu Xie, Hongling Xu +5
Fine-grained sentiment analysis (FSA) aims to extract and summarize user opinions from vast opinionated text. Recent studies demonstrate that large language models (LLMs) possess e…
DS-ABSA: Dual-Stream Data Synthesis with Label Refinement for Few-Shot Aspect-Based Sentiment Analysis
Hongling Xu, Yice Zhang, Qianlong Wang +1
Recently developed large language models (LLMs) have presented promising new avenues to address data scarcity in low-resource scenarios. In few-shot aspect-based sentiment analysis…
Self-Training with Pseudo-Label Scorer for Aspect Sentiment Quad Prediction
Yice Zhang, Jie Zeng, Weiming Hu +3
Aspect Sentiment Quad Prediction (ASQP) aims to predict all quads (aspect term, aspect category, opinion term, sentiment polarity) for a given review, which is the most representat…
WebCiteS: Attributed Query-Focused Summarization on Chinese Web Search Results with Citations
Haolin Deng, Chang Wang, Xin Li +6
Enhancing the attribution in large language models (LLMs) is a crucial task. One feasible approach is to enable LLMs to cite external sources that support their generations. Howeve…
EventRL: Enhancing Event Extraction with Outcome Supervision for Large Language Models
Jun Gao, Huan Zhao, Wei Wang +2
In this study, we present EventRL, a reinforcement learning approach developed to enhance event extraction for large language models (LLMs). EventRL utilizes outcome supervision wi…
Benchmarking Large Language Models with Augmented Instructions for Fine-grained Information Extraction
Jun Gao, Huan Zhao, Yice Zhang +3
Information Extraction (IE) is an essential task in Natural Language Processing. Traditional methods have relied on coarse-grained extraction with simple instructions. However, wit…