no code implementations • 8 Feb 2024 • Ming Shen
Although supervised finetuning (SFT) has emerged as an essential technique to align large language models with humans, it is considered superficial, with style learning being its nature.
no code implementations • 2 Oct 2023 • Man Luo, Shrinidhi Kumbhar, Ming Shen, Mihir Parmar, Neeraj Varshney, Pratyay Banerjee, Somak Aditya, Chitta Baral
This work strives to understand the proficiency of LLMs in logical reasoning by offering a brief review of the latest progress in this area; with a focus on the logical reasoning datasets, tasks, and the methods adopted to utilize LLMs for reasoning.
no code implementations • 27 Jun 2023 • Zhaohui Wei, Zhao Zhou, Peng Wang, Jian Ren, Yingzeng Yin, Gert Frølund Pedersen, Ming Shen
In this study, we proposed a deep learning-assisted and image-based intelligent modeling approach for accelerating the data acquisition of antenna samples with different physical structures.
no code implementations • 8 Jun 2023 • Martin H. Nielsen, Chia-Yi Yeh, Ming Shen, Muriel Médard
We propose to use a liquid time constant (LTC) network to predict the future blockage status of a millimeter wave (mmWave) link using only the received signal power as the input to the system.
no code implementations • 7 Jun 2023 • Martin H. Nielsen, Yufeng Zhang, Changbin Xue, Jian Ren, Yingzeng Yin, Ming Shen, Gert F. Pedersen
One key communication block in 5G and 6G radios is the active phased array (APA).
no code implementations • 21 Mar 2023 • Ming Shen, Jie Ma, Shuai Wang, Yogarshi Vyas, Kalpit Dixit, Miguel Ballesteros, Yassine Benajiba
Opinion summarization provides an important solution for summarizing opinions expressed among a large number of reviews.
no code implementations • 28 Feb 2023 • Tung Thai, Ming Shen, Mayank Garg, Ayush Kalani, Nakul Vaidya, Utkarsh Soni, Mudit Verma, Sriram Gopalakrishnan, Neeraj Varshney, Chitta Baral, Subbarao Kambhampati, Jivko Sinapov, Matthias Scheutz
Learning to detect, characterize and accommodate novelties is a challenge that agents operating in open-world domains need to address to be able to guarantee satisfactory task performance.
no code implementations • ACL 2021 • Ming Shen, Pratyay Banerjee, Chitta Baral
In this work, we propose Masked Noun-Phrase Prediction (MNPP), a pre-training strategy to tackle pronoun resolution in a fully unsupervised setting.
1 code implementation • ACL 2020 • Bill Yuchen Lin, Dong-Ho Lee, Ming Shen, Ryan Moreno, Xiao Huang, Prashant Shiralkar, Xiang Ren
In this paper, we introduce "entity triggers," an effective proxy of human explanations for facilitating label-efficient learning of NER models.
2 code implementations • Findings of the Association for Computational Linguistics 2020 • Bill Yuchen Lin, Wangchunshu Zhou, Ming Shen, Pei Zhou, Chandra Bhagavatula, Yejin Choi, Xiang Ren
In this paper, we present a constrained text generation task, CommonGen associated with a benchmark dataset, to explicitly test machines for the ability of generative commonsense reasoning.
Ranked #1 on Text Generation on CommonGen