CAREER: Flexible and Robust Reasoning in Natural Language

About This Grant

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). Modern question answering systems, embedded in search engines and digital assistants, have improved dramatically with the development of large neural network models. When a user asks a simple question, these systems can typically return an answer directly rather than just linking to a webpage. However, these systems still fail on more complex questions, and when they fail, they may mislead their users. They lack an important capability that humans have: the ability to reason about and synthesize the information they see, retrieve and integrate additional information, and arrive at a justified conclusion. This CAREER project aims to address this shortcoming by developing systems that "think through" textual evidence, leading to more reliable answers that can be explained to a user. Such advances fit into a broader thread of building trustable artificial intelligence (AI) systems that explicitly show their work and are auditable before and during their deployment. This project specifically addresses the problems of question answering and fact-checking by developing a learning-based system that reasons in natural language. The system takes text as input, then applies pre-trained neural network models to reformulate that text, derive conclusions from it, and eventually check a claim or verify an answer. This process produces a series of logically connected statements understandable by a human. This outcome is enabled by two modules. First, a deduction module repeatedly combines two statements and generates a third that follows from the inputs, encapsulating common logical rules. Second, a verifier determines whether the final deduced evidence validates the original claim. Both systems are built from pre-trained models like T5 that have demonstrated strong generalization capabilities. Collecting training data for these models constitutes a core challenge; the project's approach blends multiple strategies including synthetic data generation and human-in-the-loop annotation. These techniques are applied to the domains of question answering and fact checking, problems where providing additional explanation and justification instead of just giving a best-effort answer are essential to make usable systems. This system paves the way for NLP tools that know what they don't know, provide interpretability for end users, and enable system developers to better understand and improve their models. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.