Search Results for author: Ming Shen

Found 10 papers, 2 papers with code

Rethinking Data Selection for Supervised Fine-Tuning

no code implementations8 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.

Towards LogiGLUE: A Brief Survey and A Benchmark for Analyzing Logical Reasoning Capabilities of Language Models

no code implementations2 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.

Knowledge Distillation Language Modelling +1

Fast and Automatic 3D Modeling of Antenna Structure Using CNN-LSTM Network for Efficient Data Generation

no code implementations27 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.

Blockage Prediction in Directional mmWave Links Using Liquid Time Constant Network

no code implementations8 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.

Future prediction Time Series

Methods and Mechanisms for Interactive Novelty Handling in Adversarial Environments

no code implementations28 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.

Novelty Detection

Unsupervised Pronoun Resolution via Masked Noun-Phrase Prediction

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.

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