Pragmatic understanding of language requires common-sense reasoning: the ability to draw inferences about real-world situations, the people involved in them, and their mental states. This type of reasoning inherently involves uncertainty. While it is simple to define "correct" solutions in, e.g., mathematical reasoning benchmarks for LLMs, pinning down "correctness" in common-sense reasoning benchmarks presents a number of challenges, including cognitive biases and cross-cultural differences. In this talk, I will discuss some of my lab's research efforts to study and address these challenges.
Rachel Rudinger is an Associate Professor of Computer Science at the University of Maryland. Her research interests lie in the areas of natural language understanding, commonsense reasoning, computational semantics/pragmatics, and issues of sociocultural fairness in NLP systems. She is a recipient of the National Science Foundation CAREER Award and holds a Ph.D. in Computer Science from Johns Hopkins University.

