1 code implementation • 17 Oct 2023 • Lorenzo Jaime Yu Flores, Heyuan Huang, Kejian Shi, Sophie Chheang, Arman Cohan
Text simplification has emerged as an increasingly useful application of AI for bridging the communication gap in specialized fields such as medicine, where the lexicon is often dominated by technical jargon and complex constructs.
1 code implementation • 16 Sep 2023 • Yijie Zhou, Kejian Shi, Wencai Zhang, Yixin Liu, Yilun Zhao, Arman Cohan
Open-domain Multi-Document Summarization (ODMDS) is a critical tool for condensing vast arrays of documents into coherent, concise summaries.
1 code implementation • 23 May 2023 • Yixin Liu, Kejian Shi, Katherine S He, Longtian Ye, Alexander R. Fabbri, PengFei Liu, Dragomir Radev, Arman Cohan
Meanwhile, we perform a meta-analysis on this new learning setting that reveals a discrepancy between human and LLM-based evaluation, highlighting the benefits and risks of this LLM-as-reference setting we investigated.
1 code implementation • 16 Feb 2023 • Tomasz Korbak, Kejian Shi, Angelica Chen, Rasika Bhalerao, Christopher L. Buckley, Jason Phang, Samuel R. Bowman, Ethan Perez
Language models (LMs) are pretrained to imitate internet text, including content that would violate human preferences if generated by an LM: falsehoods, offensive comments, personally identifiable information, low-quality or buggy code, and more.
1 code implementation • ACL 2022 • Aliva Das, Xinya Du, Barry Wang, Kejian Shi, Jiayuan Gu, Thomas Porter, Claire Cardie
Document-level information extraction (IE) tasks have recently begun to be revisited in earnest using the end-to-end neural network techniques that have been successful on their sentence-level IE counterparts.
no code implementations • 1 Aug 2020 • Haoran Su, Kejian Shi, Li Jin, Joseph Y. J. Chow
Emergency vehicle (EMV) service is a key function of cities and is exceedingly challenging due to urban traffic congestion.
no code implementations • 2 Mar 2020 • Haoran Su, Kejian Shi, Joseph. Y. J. Chow, Li Jin
Based on pairs of neural networks representing actors and critics for agent vehicles, we develop a multi-agent actor-critic deep reinforcement learning algorithm that handles a varying number of vehicles and a random proportion of connected vehicles in the traffic.