Bing He

Bing He

Principle in Action and Forge Ahead

Publications [Google Scholar]

Code, data, and downloadable datasets are linked below wherever available.

  1. (EMNLP 2026) Minhua Lin, Juncheng Wu, Zijun Wang, Zhan Shi, Yisi Sang, Bing He, Zewen Liu, Tianxin Wei, Zongyu Wu, Zhiwei Zhang, Dakuo Wang, Xiang Zhang, Benoit Dumoulin, Cihang Xie, Yuyin Zhou, Suhang Wang, Hanqing Lu, “Harness Updating Is Not Harness Benefit: Disentangling Evolution Capabilities in Self-Evolving LLM Agents”, code and data, paper, The Conference on Empirical Methods in Natural Language Processing 2026, Main Conference
  2. (EMNLP 2026) Tianxin Wei, Zhan Shi, Minhua Lin, Bing He, Zewen Liu, Yisi Sang, Yuanchen Bei, Xuying Ning, Jiaru Zou, Ting-Wei Li, Xiao Lin, Yanjun Zhao, Chi Wang, Benoit Dumoulin, Dakuo Wang, Jingrui He, Hanqing Lu, “Evo-Harness: Context-to-Harness Skill Compilation for Self-Evolving Agents”, code and data, paper, The Conference on Empirical Methods in Natural Language Processing 2026, Main Conference
  3. (ICLR 2026) Pengfei He, Zhenwei Dai, Bing He, Hui Liu, Xianfeng Tang, Hanqing Lu, Juanhui Li, Jiayuan Ding, Subhabrata Mukherjee, Suhang Wang, Yue Xing, Jiliang Tang, Benoit Dumoulin, “TRAJECT-Bench: A Trajectory-Aware Benchmark for Evaluating Agentic Tool Use”, code and data, paper, The International Conference on Learning Representations 2026
  4. (ACL 2026) Yingqian Cui, Zhenwei Dai, Pengfei He, Bing He, Hui Liu, Zhan Shi, Xianfeng Tang, Jingying Zeng, Suhang Wang, Yue Xing, Jiliang Tang, Benoit Dumoulin, “A Reward-Guided Dual-Phase Framework for Adaptive Inference-Time Reasoning”, paper, The 64th Annual Meeting of the Association for Computational Linguistics 2026, Main Findings
  5. (TKDD 2025) Bing He, Yibo Hu, Yeon-Chang Lee, Soyoung Oh, Gaurav Verma, Srijan Kumar, “A Survey on the Role of Crowds in Combating Online Misinformation: Annotators, Evaluators, and Creators”, paper collections, survey, ACM Transactions on Knowledge Discovery from Data, January 2025
  6. (WWW 2024) Bing He, Sreyashi Nag, Limeng Cui, Suhang Wang, Zheng Li, Rahul Goutam, Zhen Li and Haiyang Zhang, “Hierarchical Query Classification in E-commerce Search”, paper, The ACM Web Conference 2024 (Acceptance Rate: 21.3%)
  7. (WebSci 2024) Bing He, Yingchen Ma, Mustaque Ahamad, Srijan Kumar, “Corrective or Backfire: Characterizing and Predicting User Response to Social Correction”, code and data, downloadable dataset, The ACM Web Science Conference 2024
  8. (WWW 2023) Bing He, Mustaque Ahamad, Srijan Kumar, “Reinforcement Learning-based Counter-Misinformation Response Generation: A Case Study of COVID-19 Vaccine Misinformation”, code and data, paper, downloadable dataset, The ACM Web Conference 2023 (Acceptance Rate: 365/1900=19.2%)
  9. (WebSci 2023) Yingchen Ma, Bing He, Nathan Subrahmanian, Srijan Kumar, “Characterizing and Predicting Social Correction on Twitter”, code and data, paper, downloadable dataset, The ACM Web Science Conference 2023
  10. (KDD 2021) Bing He, Mustaque Ahamad, Srijan Kumar, “PETGEN: Personalized Text Generation Attack on Deep Sequence Embedding-based Classification Models”, code and data, paper, ACM SIGKDD Conference on Knowledge Discovery and Data Mining 2021 (Acceptance Rate: 238/1541=15.4%)
  11. (ASONAM 2021) Bing He, Caleb Ziems, Sandeep Soni, Naren Ramakrishnan, Diyi Yang, Srijan Kumar, “Racism is a virus: Anti-asian Hate and Counterspeech in Social Media during the Covid-19 Crisis”, code and data, paper, downloadable dataset (tweets), (geolocations), IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2021
  12. (IEEE BigData 2020) Nicholas Micallef∗, Bing He∗, Srijan Kumar, Mustaque Ahamad, Nasir Memon, “The Role of the Crowd in Countering Misinformation: A Case Study of the COVID-19 Infodemic”, code and data, paper, downloadable dataset (annotated tweets), (raw tweets), IEEE International Conference on Big Data 2020 (∗ equal contribution)
  13. (ECML-PKDD 2018) Bing He, Xiaolin Chen, Dian Zhang, Siyuan Liu, Dawei Han, Lionel M Ni, “PBE: Driver Behavior Assessment Beyond Trajectory Profiling”, paper, The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2018
  14. (IEEE BigData 2018) Bing He, Dian Zhang, Siyuan Liu, Hao Liu, Dawei Han, Lionel M Ni, “Profiling driver behavior for personalized insurance pricing and maximal profit”, paper, IEEE International Conference on Big Data 2018