no code implementations • 1 Nov 2023 • Zhiwei Liu, Tianlin Zhang, Kailai Yang, Paul Thompson, Zeping Yu, Sophia Ananiadou
The emotions and sentiments of netizens, as expressed in social media posts and news, constitute important factors that can help to distinguish fake news from genuine news and to understand the spread of rumors.
no code implementations • 19 Dec 2023 • Zeping Yu, Sophia Ananiadou
Base on our methods, we find where factual knowledge <France, capital, Paris> is stored.
no code implementations • 5 Feb 2024 • Zeping Yu, Sophia Ananiadou
In shallow layers, the features of demonstrations are merged into their corresponding labels, and the features of the input text are aggregated into the last token.
1 code implementation • 16 Jan 2024 • Zhiwei Liu, Kailai Yang, Tianlin Zhang, Qianqian Xie, Zeping Yu, Sophia Ananiadou
In this paper, we propose EmoLLMs, the first series of open-sourced instruction-following LLMs for comprehensive affective analysis based on fine-tuning various LLMs with instruction data, the first multi-task affective analysis instruction dataset (AAID) with 234K data samples based on various classification and regression tasks to support LLM instruction tuning, and a comprehensive affective evaluation benchmark (AEB) with 14 tasks from various sources and domains to test the generalization ability of LLMs.
1 code implementation • NeurIPS 2020 • Zeping Yu, Wenxin Zheng, Jiaqi Wang, Qiyi Tang, Sen Nie, Shi Wu
We adopt Deep Pyramid Convolutional Neural Network (DPCNN) for source code feature extraction and Graph Neural Network (GNN) for binary code feature extraction.
3 code implementations • COLING 2018 • Zeping Yu, Gongshen Liu
In this paper, we introduce sliced recurrent neural networks (SRNNs), which could be parallelized by slicing the sequences into many subsequences.
Ranked #6 on Sentiment Analysis on Amazon Review Full
2 code implementations • IJCAI 2019 • Zeping Yu, Jianxun Lian, Ahmad Mahmoody, Gongshen Liu, Xing Xie
User modeling is an essential task for online rec- ommender systems.
Ranked #2 on Recommendation Systems on Amazon Product Data