About Me
I’m an Applied Scientist at Amazon. I did my Ph.D. at Arizona State University, where I’m advised by Chitta Baral and Ben Zhou. My research focuses on natural language processing, particularly how synthetic data can help train more effective NLP systems. Previously, I earned my M.S. in Computer Science from the University of Southern California and my B.S. in Computer Science from Worcester Polytechnic Institute. Before joining Amazon full-time, I completed several applied science internships with the company.
Publications
BOW: Training Language Models to Reason Over Plausible Next Words
Ming Shen*, Zhikun Xu*, Jacob Dineen, Xiao Ye, Ben Zhou
in Proceedings of EMNLP 2026
Relational Priors as Convergence Pressure in LLM-Based Multi-Agent Systems
Ming Shen, Chao Shang, Sadat Shahriar, Devang Kulshreshtha, Yi Zhang, Sandesh Swamy, Yanjun Qi
in Proceedings of AACL 2026 Oral
Optimizing LLM-Based Multi-Agent System with Textual Feedback: A Case Study on Software Development
Ming Shen, Raphael Shu, Anurag Pratik, James Gung, Yubin Ge, Monica Sunkara, Yi Zhang
COLM 2025 Workshop on AI Agents: Capabilities and Safety
ToW: Thoughts of Words Improve Reasoning in Large Language Models
Zhikun Xu*, Ming Shen*, Jacob Dineen, Zhaonan Li, Xiao Ye, Shijie Lu, Aswin RRV, Chitta Baral, Ben Zhou
in Proceedings of NAACL 2025
QA-LIGN: Aligning LLMs through Constitutionally Decomposed QA
Jacob Dineen, Aswin RRV, Qin Liu, Zhikun Xu, Xiao Ye, Ming Shen, Zhaonan Li, Shijie Lu, Chitta Baral, Muhao Chen, Ben Zhou
in Findings of EMNLP 2025
Rethinking Data Selection for Supervised Fine-Tuning
Ming Shen
arXiv, 2024
Towards LogiGLUE: A Brief Survey and A Benchmark for Analyzing Logical Reasoning Capabilities of Language Models
Man Luo, Shrinidhi Kumbhar, Ming Shen, Mihir Parmar, Neeraj Varshney, Chitta Baral, Pratyay Banerjee, Somak Aditya
arXiv, 2023
Simple Yet Effective Synthetic Dataset Construction for Unsupervised Opinion Summarization
Ming Shen, Jie Ma, Shuai Wang, Yogarshi Vyas, Kalpit Dixit, Miguel Ballesteros, Yassine Benajiba
in Findings of EACL 2023
Unsupervised Pronoun Resolution via Masked Noun-Phrase Prediction
Ming Shen, Pratyay Banerjee, and Chitta Baral
in Proceedings of ACL 2021
CommonGen: A Constrained Text Generation Challenge for Generative Commonsense Reasoning
Bill Yuchen Lin, Wangchunshu Zhou, Ming Shen, Pei Zhou, Chandra Bhagavatula, Yejin Choi, and Xiang Ren
in Findings of EMNLP 2020
TriggerNER: Learning with Entity Triggers as Explanations for Named Entity Recognition
Bill Yuchen Lin, Dong-Ho Lee, Ming Shen, Ryan Moreno, Xiao Huang, Prashant Shiralkar, and Xiang Ren
in Proceedings of ACL 2020
Evaluating Medical LLMs by Levels of Autonomy: A Survey Moving from Benchmarks to Applications
Xiao Ye, Jacob Dineen, Zhaonan Li, Zhikun Xu, Weiyu Chen, Shijie Lu, Yuxi Huang, Ming Shen, Phu Tran, Ji-Eun Irene Yum, Muhammad Ali Khan, Muhammad Umar Afzal, Irbaz Bin Riaz, Ben Zhou
arXiv, 2025
Methods and Mechanisms for Interactive Novelty Handling in Adversarial Environments
Tung Thai, Ming Shen, Mayank Garg, Ayush Kalani, Nakul Vaidya, Utkarsh Soni, Mudit Verma, Sriram Gopalakrishnan, Chitta Baral, Subbarao Kambhampati, Jivko Sinapov, Matthias Scheutz
in Proceedings of AAMAS 2023
An Architecture for Novelty Handling in a Multi-Agent Stochastic Environment: Case Study in Open-World Monopoly
Tung Thai, Ming Shen, Neeraj Varshney, Sriram Gopalakrishnan, Utkarsh Soni, Matthias Scheutz, Chitta Baral, and Jivko Sinapov
in Proceedings of AAAI 2022 Symposium: Designing Artificial Intelligence for Open Worlds
Projects
Demo project for ACL 2019 System Demonstrations paper: AlpacaTag: Active Learning-based Crowd Annotation Framework for Sequence Tagging
Service
Reviewer: ACL, EMNLP, EACL, COLING, WWW