no code implementations • Findings (ACL) 2022 • ZeFeng Cai, LinLin Wang, Gerard de Melo, Fei Sun, Liang He
Generating explanations for recommender systems is essential for improving their transparency, as users often wish to understand the reason for receiving a specified recommendation.
no code implementations • 11 Apr 2024 • Yansheng Li, Kun Li, Yongjun Zhang, LinLin Wang, Dingwen Zhang
To fill in the gap of the overhead view dataset, this paper constructs and releases an aerial image urban scene graph generation (AUG) dataset.
no code implementations • 23 Feb 2024 • Xin Yi, LinLin Wang, Xiaoling Wang, Liang He
In this paper, we propose fine-grained detoxification via instance-level prefixes (FGDILP) to mitigate toxic text without additional cost.
no code implementations • 20 Jan 2024 • LinLin Wang, Mingxue Quan, Wei Wang, Dezhao Wang, Shanwen Wang
In the era of Big Data, prompt analysis and processing of data sets is critical.
no code implementations • 20 Dec 2023 • Yan Cai, LinLin Wang, Ye Wang, Gerard de Melo, Ya zhang, Yanfeng Wang, Liang He
The emergence of various medical large language models (LLMs) in the medical domain has highlighted the need for unified evaluation standards, as manual evaluation of LLMs proves to be time-consuming and labor-intensive.
no code implementations • 7 Oct 2021 • Zijing Yang, Jiabo Ye, LinLin Wang, Xin Lin, Liang He
To achieve this, existing approaches take advantage of the knowledge graphs to learn more evidences for inference, whereas they often suffer from invalid reasoning for lack of elegant decision making strategies.
no code implementations • SEMEVAL 2021 • Pingsheng Liu, LinLin Wang, Qian Zhao, Hao Chen, Yuxi Feng, Xin Lin, Liang He
This paper describes our system for SemEval-2021 Task 4: Reading Comprehension of Abstract Meaning.
1 code implementation • SEMEVAL 2020 • Qian Zhao, Siyu Tao, Jie zhou, LinLin Wang, Xin Lin, Liang He
As a result, this model performs quite well in both validation and explanation.