Reasoning in the Wild

Title: Reasoning in the Wild

Abstract: In this talk, I will discuss how to build natural language processing (NLP) systems that solve real-world problems requiring complex reasoning. I will address three key challenges. First, because real-world reasoning tasks often differ from the data used in pretraining, I will introduce WildChat, a dataset of reasoning questions collected from users, and demonstrate how training on it enhances language models’ reasoning abilities. Second, because supervision is often limited in practice, I will describe my approach to enabling models to perform multi-hop reasoning without direct supervision. Finally, since many real-world applications demand reasoning beyond natural language, I will introduce a language agent capable of acting on external feedback. I will conclude by outlining a vision for training the next generation of AI reasoning models.

Bio: Wenting Zhao is a Ph.D. candidate in Computer Science at Cornell University, advised by Claire Cardie and Sasha Rush. Her research focuses on the intersection of natural language processing and reasoning, where she develops techniques to effectively reason over real-world scenarios. Her work has been featured in The Washington Post and TechCrunch. She has co-organized several tutorials and workshops, including the VerifAI: AI Verification in the Wild workshop at ICLR 2025 and the Complex Reasoning in Natural Language tutorial at ACL 2023. In 2024, she was recognized as a rising star in Generative AI and was named Intern of the Year at the Allen Institute for AI in 2023.