Using Large Language Models to Generate, Validate, and Apply User Intent Taxonomies

no code implementations • 14 Sep 2023 • Chirag Shah, Ryen W. White, Reid Andersen, Georg Buscher, Scott Counts, Sarkar Snigdha Sarathi Das, Ali Montazer, Sathish Manivannan, Jennifer Neville, Xiaochuan Ni, Nagu Rangan, Tara Safavi, Siddharth Suri, Mengting Wan, Leijie Wang, Longqi Yang

However, using LLMs to generate a user intent taxonomy and apply it for log analysis can be problematic for two main reasons: (1) such a taxonomy is not externally validated; and (2) there may be an undesirable feedback loop.

Shadow-Oriented Tracking Method for Multi-Target Tracking in Video-SAR

no code implementations • 29 Nov 2022 • Xiaochuan Ni, Xiaoling Zhang, Xu Zhan, Zhenyu Yang, Jun Shi, Shunjun Wei, Tianjiao Zeng

To avoid missed tracking, a detection method based on deep learning is designed to thoroughly learn shadows' features, thus increasing the accurate estimation.

Paragraph-level Neural Question Generation with Maxout Pointer and Gated Self-attention Networks

1 code implementation • EMNLP 2018 • Yao Zhao, Xiaochuan Ni, Yuanyuan Ding, Qifa Ke

Long text has posed challenges for sequence to sequence neural models in question generation {--} worse performances were reported if using the whole paragraph (with multiple sentences) as the input.

Question Answering Question Generation +4