RESEARCH INTERESTS
Neuro-symbolic reasoning methods: I am interested in how symbolic methods and statistic learning can jointly teach models to conduct complex reasoning on knowledge and information. This also covers how models can acquire, encode, and apply knowledge to solve various problems.
Large language model NLP: I am fascinated by how large language models like GPT-3 can encode vast information and generate fluent text. I want to explore how these general purpose models can be used in downstream NLP tasks such as open-domain QA and commonsense reasoning. I am interested in building systems that allow general purpose models to be used in a dynamic real-life setting.
Interpretability, benchmarking, and verified AI: I want to develop new tools and theories that help interpret and probe model behaviors. I also want to build benchmarks that can evaluate models' ability and diagnose potential issues in data and learning, especially asses how reliable a model can be in real-life use cases. I recently started to explore the idea of verified AI, where the goal is to have provable assurances of correctness concerning mathematically-specified requirements.
Design new learning algorithms: How machines can learn to understand and reason similarly to human learning is still open for exploration. I want to design new learning algorithms that help models learn continually, actively, comprehensively, and transparently by drawing inspiration from human cognition.