publications

2025

1. LLMs

   

   Open domain question answering with conflicting contexts

   Siyi Liu, Qiang Ning, Kishaloy Halder, Wei Xiao, Zheng Qi, Phu Mon Htut, Yi Zhang, Neha Anna John, Bonan Min, and 2 more authors

   Findings of NAACL, 2025

   arXiv Bib Code

   @article{liu2024open,
     title = {Open domain question answering with conflicting contexts},
     author = {Liu, Siyi and Ning, Qiang and Halder, Kishaloy and Xiao, Wei and Qi, Zheng and Htut, Phu Mon and Zhang, Yi and John, Neha Anna and Min, Bonan and Benajiba, Yassine and Roth, Dan},
     journal = {Findings of NAACL},
     year = {2025}
   }

2. LLMs

   

   Aligning to Constraints for Data-Efficient Language Model Customization

   Fei Wang, Chao Shang, Shuai Wang, Sarthak Jain, Qiang Ning, Bonan Min, Vittorio Castelli, Yassine Benajiba, and Dan Roth

   Findings of NAACL, 2025

   arXiv Bib

   @article{wang-etal-2025-aligning,
     title = {Aligning to Constraints for Data-Efficient Language Model Customization},
     author = {Wang, Fei and Shang, Chao and Wang, Shuai and Jain, Sarthak and Ning, Qiang and Min, Bonan and Castelli, Vittorio and Benajiba, Yassine and Roth, Dan},
     journal = {Findings of NAACL},
     year = {2025}
   }

2022

1. NLP

   

   PInKS: Preconditioned commonsense inference with minimal supervision

   Ehsan Qasemi, Piyush Khanna, Qiang Ning, and Muhao Chen

   AACL, 2022

   arXiv Bib Code

   @article{qasemi2022pinks,
     title = {PInKS: Preconditioned commonsense inference with minimal supervision},
     author = {Qasemi, Ehsan and Khanna, Piyush and Ning, Qiang and Chen, Muhao},
     journal = {AACL},
     year = {2022}
   }

2. NLP

   

   Answer Consolidation: Formulation and Benchmarking

   Wenxuan Zhou, Qiang Ning, Heba Elfardy, Kevin Small, and Muhao Chen

   NAACL, 2022

   Abs arXiv Bib Video Code Slides Website

   Current question answering (QA) systems primarily consider the single-answer scenario, where each question is assumed to be paired with one correct answer. However, in many real-world QA applications, multiple answer scenarios arise where consolidating answers into a comprehensive and non-redundant set of answers is a more efficient user interface. In this paper, we formulate the problem of answer consolidation, where answers are partitioned into multiple groups, each representing different aspects of the answer set. Then, given this partitioning, a comprehensive and non-redundant set of answers can be constructed by picking one answer from each group. To initiate research on answer consolidation, we construct a dataset consisting of 4,699 questions and 24,006 sentences and evaluate multiple models. Despite a promising performance achieved by the best-performing supervised models, we still believe this task has room for further improvements.

   @article{zhou-etal-2022-answer,
     title = {Answer Consolidation: Formulation and Benchmarking},
     author = {Zhou, Wenxuan and Ning, Qiang and Elfardy, Heba and Small, Kevin and Chen, Muhao},
     journal = {NAACL},
     year = {2022}
   }

2020

1. Theory

   

   Learnability with Indirect Supervision Signals

   Kaifu Wang, Qiang Ning, and Dan Roth

   NeurIPS, 2020

   arXiv Bib Video Poster Slides

   @article{NEURIPS2020_67ff32d4,
     author = {Wang, Kaifu and Ning, Qiang and Roth, Dan},
     journal = {NeurIPS},
     title = {Learnability with Indirect Supervision Signals},
     year = {2020}
   }

2. NLP

   

   Evaluating models’ local decision boundaries via contrast sets

   Matt Gardner, Yoav Artzi, Victoria Basmova, Jonathan Berant, Ben Bogin, Sihao Chen, Pradeep Dasigi, Dheeru Dua, Yanai Elazar, and 2 more authors

   Findings of EMNLP, 2020

   arXiv Bib Code

   @article{gardner2020evaluating,
     title = {Evaluating models' local decision boundaries via contrast sets},
     author = {Gardner, Matt and Artzi, Yoav and Basmova, Victoria and Berant, Jonathan and Bogin, Ben and Chen, Sihao and Dasigi, Pradeep and Dua, Dheeru and Elazar, Yanai and Gottumukkala, Ananth and others},
     journal = {Findings of EMNLP},
     year = {2020}
   }

2019

1. NLP

   

   "Going on a vacation" takes longer than" Going for a walk": A Study of Temporal Commonsense Understanding

   Ben Zhou, Daniel Khashabi, Qiang Ning, and Dan Roth

   EMNLP, 2019

   arXiv Bib PDF Code Slides Website

   @article{zhou2019going,
     title = {"Going on a vacation" takes longer than" Going for a walk": A Study of Temporal Commonsense Understanding},
     author = {Zhou, Ben and Khashabi, Daniel and Ning, Qiang and Roth, Dan},
     journal = {EMNLP},
     year = {2019}
   }