I am a Ph.D. student in Computer Science at Cornell University. I am advised by Claire Cardie, with additional mentorship from Thorsten Joachims, Kianté Brantley, Daniel D. Lee and many other amazing collaborators. Outside of Cornell, I have spent time at Microsoft Research as a research intern, working with Dipendra Misra. Prior to Cornell, I worked with Luke Zettlemoyer and Yejin Choi at the University of Washington.
My primary research interests are natural language processing and machine learning. My research develops AI systems designed for effective real-world use. Standard practices in AI assume tasks are fully understood and clearly specified from the start, which is often violated in practice. Users often find it difficult to communicate their full range of needs and expectations, while models struggle to learn from and adapt to the practical objective once deployed. Although recent advances in instruction following research partially address these issues, their training requires preference data collected from paid annotators, do not enable models to continually improve post-deployment, and still depend on users’ prompt engineering skills.
To overcome these challenges, I design LLM systems that learn from naturally occurring user feedback in deployment, and develop adaptable, reliable methods to enhance their practical utility. My recent research includes:
- Continual learning from user interaction via explicit feedback in model deployment [1, 2]
- Interactive learning for LLM agents based on implicit user edits to infer latent user preference [3]
- End-to-end optimization of LLM-based retrieval pipelines based on practical feedback to the end task [4]
[Google Scholar] [Semantic Scholar] [GitHub] [Twitter]
Check out our MiniTorch project – a Python re-implementation of the Torch API. It is designed as a teaching library to teach machine learning engineers 1) core deep learning concepts such as tensor operations, autograd, and CUDA programming, and 2) how to engineer machine learning systems to be correct, robust, and fast. We built this DIY deep learning library primarily for the Machine Learning Engineering course at Cornell Tech.
@12/2024 Talk at Berkeley.
@10/2024 Poster presentation at 15th Annual Machine Learning Symposium in NYC.
@10/2024 Attending COLM 2024 in Philadelphia.
@10/2024 Talk at NYU.
@09/2024 Talk at Princeton University.
@09/2024 Talk at Columbia University. Recording is available online.
Research
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I Could’ve Asked That: Reformulating Unanswerable Questions Wenting Zhao, Ge Gao, Claire Cardie, and Sasha Rush |
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Aligning LLM Agents by Learning Latent Preference from User Edits Ge Gao*, Alexey Taymanov*, Eduardo Salinas, Paul Mineiro, and Dipendra Misra |
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Policy-Gradient Training of Language Models for Ranking Ge Gao, Jonathan D. Chang, Claire Cardie, Kianté Brantley, and Thorsten Joachims |
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Continually Improving Extractive QA via Human Feedback Ge Gao*, Hung-Ting Chen*, Yoav Artzi, and Eunsol Choi |
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Simulating Bandit Learning from User Feedback for Extractive QA Ge Gao, Eunsol Choi, and Yoav Artzi |
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MiniTorch: Teaching Library for Python Re-implementation of the Torch API Sasha Rush, Ge Gao, Anton Abilov, and Aaron Gokaslan |
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Neural Metaphor Detection in Context Ge Gao, Eunsol Choi, Yejin Choi, and Luke Zettlemoyer |
* denotes equal first authorship.
Contact
ggao asperand cs dot cornell dot edu
2 West Loop Rd, Cornell Tech, NYC
Misc
- You could pronounce my first name as /gə/ or G.
- If interested, read more about my full name in classical Chinese literature.