Hello! I am a final-year PhD student at the Paul G. Allen School of Computer Science & Engineering, University of Washington. I am fortunate to be co-advised by Hannaneh Hajishirzi and Noah Smith. I am also a student researcher at AI2. I have previously interned at Meta AI and Microsoft Research Asia. Prior to UW, I did a master at Peking University and an undergraduate at Shanghai Jiao Tong University.

These days, I am excited about data-centric approaches for understanding and advancing modern artificial intelligence systems. I believe that data can serve as an effective, sustainable, auditable, and beneficial ground for future human AI collaboration and dual improvement. Here are some topics I have been thinking about recently:

• Data Curation. Large-scale data curation has powered several AI breakthoughs. I have led/contributed to several popular datasets, including Super-NaturalInstructions for unifying NLP tasks, MultiModalQA for multimodal question answering, DROP for machine reasoning, and DuReader for machine reading comprehension. I am looking forward to new paradigms and algorithms for constructing and maintaining high-quality data, particularly for more complex scenarios.
• Synthetic Data. Generative models have enabled producing data easily at scale. Our work Self-Instruct has pioneered the idea of using model-generated data to improve models. It also raised many interesting questions, such as: Can models really achieve self-improvement? Can models generate meaningful information that does not exist before? How can we design better data synthesis pipelines and algorithms?
• Data Attribution. I envision a future where individuals and organizations can collaborately contribute data to AI systems, and benefit in return when their data takes effect. This requires a new data attribution framework that is fair, transparent, and efficient. I am interested in the development of such a framework and the required algorithms, as well as the potential impact on society.
• Open Science of Language Models. I believe that openness is the scientific, trustworthy, and sustainable way to advance language models. Throughout my PhD, I have been actively constributing to the development of open language models (OLMo) at AI2. I also co-led the Tülu series of work(1, 2, and 2.5), exploring post-training techniques. We release all our artifacts to facilitate open understanding and research of language models.