Yilun Zhou

I currently work at Salesforce as a research scientist. Before that, I worked at Amazon as an applied scientist. I received my Ph.D. degree at MIT Department of Electrical Engineering and Computer Science (EECS), advised by Prof. Julie Shah, from which I also received my Master of Science degree in 2019. I received my Bachelor of Science in Engineering degree from Duke University, with double major in Computer Sciene and Electrical & Computer Engineering. I worked with Prof. George Konidaris and Prof. Kris Hauser on my undergraduate research on robotics.

My long-term research goal is to make machine learning models and systems reliable and responsible. These days, I am mainly working on three particular directions:

• LLM reasoning, especially around LLM-as-judges, [Show Only]
• (Mechanistic) interpretability of LLM, [Show Only]
• Trustworthy and societal implications for LLMs. [Show Only]

Publications [Show Highlighted]

^* Equal Contribution

• Evaluating Judges as Evaluators: The JETTS Benchmark of LLM-as-Judges as Test-Time Scaling Evaluators
  Yilun Zhou^*, Austin Xu^*, Peifeng Wang, Caiming Xiong, Shafiq Joty
  International Conference on Machine Learning (ICML), 2025
  [Paper] [Code]
• Search Engines in the AI Era: A Qualitative Understanding to the False Promise of Factual and Verifiable Source-Cited Responses in LLM-based Search
  Pranav Narayanan Venkit, Philippe Laban, Yilun Zhou, Yixin Mao, Chien-Sheng Wu
  ACM Conference on Fairness, Accountability, and Transparency (FAccT), 2025
  [Paper]
• BingoGuard: LLM Content Moderation Tools with Risk Levels
  Fan Yin, Philippe Laban, Xiangyu Peng, Yilun Zhou, Yixin Mao, Vaibhav Vats, Linnea Ross, Divyansh Agarwal, Caiming Xiong, Chien-Sheng Wu
  International Conference on Learning Representations (ICLR), 2025
  [Paper]
• Direct Judgement Preference Optimization
  Peifeng Wang^*, Austin Xu^*, Yilun Zhou, Caiming Xiong, Shafiq Joty
  arXiv preprint: 2409.14664, 2024
  [Paper]
• Shared Imagination: LLMs Hallucinate Alike
  Yilun Zhou, Caiming Xiong, Silvio Savarese, Chien-Sheng Wu
  arXiv preprint: 2407.16604, 2024
  [Paper] [Website]
• MobileAIBench: Benchmarking LLMs and LMMs for On-Device Use Cases
  Rithesh Murthy^*, Liangwei Yang^*, Juntao Tan^*, Tulika Manoj Awalgaonkar^*, Yilun Zhou, Shelby Heinecke, Sachin Desai, Jason Wu, Ran Xu, Sarah Tan, Jianguo Zhang, Zhiwei Liu, Shirley Kokane, Zuxin Liu, Ming Zhu, Huan Wang, Caiming Xiong, Silvio Savarese
  arXiv preprint: 2406.10290, 2024
  [Paper]
• A Practical Review of Mechanistic Interpretability for Transformer-Based Language Models
  Daking Rai, Yilun Zhou, Shi Feng, Abulhair Saparov, Ziyu Yao
  arXiv preprint: 2407.02646, 2024
  [Paper] [Website]
• CHAMP: A Competition-Level Dataset for Fine-Grained Analyses of LLMs' Mathematical Reasoning Capabilities
  Yujun Mao, Yoon Kim, Yilun Zhou
  Annual Meeting of the Association for Computational Linguistics (ACL) Findings, 2024
  Preliminary version in NeurIPS 2023 Workshop on Mathematical Reasoning and AI (MATH-AI)
  [Paper] [Code] [Website]
• Evaluating the Utility of Model Explanations for Model Development
  Shawn Im, Jacob Andreas, Yilun Zhou
  NeurIPS Workshop on Attributing Model Behavior at Scale (ATTRIB), 2023
  [Paper]
• Can Large Language Models Explain Themselves? A Study of LLM-Generated Self-Explanations
  Shiyuan Huang, Siddarth Mamidanna, Shreedhar Jangam, Yilun Zhou, Leilani H. Gilpin
  arXiv preprint: 2310.11207, 2023
  [Paper]
• Iterative Partial Fulfillment of Counterfactual Explanations: Benefits and Risks
  Yilun Zhou
  AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (AIES), 2023
  [Paper]
• Improving Generalization in Language Model-Based Text-to-SQL Semantic Parsing: Two Simple Semantic Boundary-Based Techniques
  Daking Rai, Bailin Wang, Yilun Zhou, Ziyu Yao
  Annual Meeting of the Association for Computational Linguistics (ACL), 2023
  [Paper] [Code]
• Techniques for Interpretability and Transparency of Black-Box Models
  Yilun Zhou
  MIT Ph.D. Thesis, 2023
  [Thesis]
• The Solvability of Interpretability Evaluation Metrics
  Yilun Zhou, Julie Shah
  Conference of the European Chapter of the Association for Computational Linguistics (EACL) Findings, 2023
  [Paper] [Code] [Website]
• Explaining Large Language Model-Based Neural Semantic Parsers
  Daking Rai, Yilun Zhou, Bailin Wang, Ziyu Yao
  AAAI Conference on Artificial Intelligence: Student Abstract and Poster Program, 2023
  [Paper]
• ExSum: From Local Explanations to Model Understanding
  Yilun Zhou, Marco Tulio Ribeiro, Julie Shah
  Conference of the North American Chapter of the Association for Computational Linguistics - Human Language Technologies (NAACL-HLT), 2022
  [Paper] [Code] [Video] [Website] [MIT News]
• The Irrationality of Neural Rationale Models
  Yiming Zheng, Serena Booth, Julie Shah, Yilun Zhou
  NAACL Workshop on Trustworthy Natural Language Processing (TrustNLP), 2022
  [Paper] [Poster] [Code]
• Do Feature Attribution Methods Correctly Attribute Features?
  Yilun Zhou, Serena Booth, Marco Tulio Ribeiro, Julie Shah
  AAAI Conference on Artificial Intelligence (AAAI), 2022
  Preliminary version in NeurIPS 2021 Workshop on Explainable AI Approaches for Debugging and Diagnosis
  [Paper] [Poster] [Code] [Video] [Website] [MIT News]
• Long-Term Resource Allocation Fairness in Average Markov Decision Process (AMDP) Environment
  Ganesh Ghalme^*, Vineet Nair^*, Vishakha Patil^*, Yilun Zhou^*
  International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS), 2022
  [Paper] [Code] [Website]
• Latent Space Alignment Using Adversarially Guided Self-Play
  Mycal Tucker, Yilun Zhou, Julie Shah
  International Journal of Human-Computer Interaction (IJHCI), 2022
  [Paper]
• RoCUS: Robot Controller Understanding via Sampling
  Yilun Zhou, Serena Booth, Nadia Figueroa, Julie Shah
  Conference on Robot Learning (CoRL), 2021
  [Paper] [Poster] [Code] [Video] [Website]
• Bayes-TrEx: a Bayesian Sampling Approach to Model Transparency by Example
  Serena Booth^*, Yilun Zhou^*, Ankit Shah, Julie Shah
  AAAI Conference on Artificial Intelligence (AAAI), 2021
  Preliminary version in AAAI 2020 Workshop on Statistical Relational AI
  [Paper] [Poster] [Code] [MIT News]
• Towards Understanding the Behaviors of Optimal Deep Active Learning Algorithms
  Yilun Zhou, Adithya Renduchintala, Xian Li, Sida Wang, Yashar Mehdad, Asish Ghoshal
  International Conference on Artificial Intelligence and Statistics (AISTATS), 2021
  [Paper] [Poster] [Code] [Video]
• Learning Household Task Knowledge from WikiHow Descriptions
  Yilun Zhou, Julie Shah, Steven Schockaert
  International Joint Conference on Artificial Intelligence (IJCAI) Workshop on Semantic Deep Learning, 2019
  [Paper] [Code]
• Predicting ConceptNet Path Quality Using Crowdsourced Assessments of Naturalness
  Yilun Zhou, Steven Schockaert, Julie Shah
  The Web Conference (WWW), 2019
  [Paper] [Code]
• Representing, Learning, and Controlling Complex Object Interactions
  Yilun Zhou, Benjamin Burchfiel, George Konidaris
  Autonomous Robots (AuRo), 2018
  Original version in Robotics: Science and Systems (RSS), 2016
  [Paper] [Video]
• 6DOF Grasp Planning by Optimizing a Deep Learning Scoring Function
  Yilun Zhou, Kris Hauser
  Robotics: Science and Systems (RSS) Workshop on Revisiting Contact - Turning a Problem into a Solution, 2017
  [Paper] [Poster]
• Incorporating Side-Channel Information into Convolutional Neural Networks for Robotic Tasks
  Yilun Zhou, Kris Hauser
  IEEE International Conference on Robotics and Automation (ICRA), 2017
  [Paper] [Code]
• Asymptotically Optimal Planning by Feasible Kinodynamic Planning in a State-Cost Space
  Kris Hauser, Yilun Zhou
  IEEE Transactions on Robotics (TRO), 2016
  [Paper] [Code] [Website]