Thomas Hartvigsen

A common goal in analyzing time series data is to understand how events cause observed variations. We study whether Large Language Models (LLMs) can infer natural language events associated with time series data.We introduce an automated method for generating tasks that test a model’s ability to...

Math word problems (MWPs) are critical K-12 educational tools, and customizing them to students’ interests and ability levels can enhance learning. However, teachers struggle to find time to customize MWPs for students given large class sizes and increasing burnout. We propose that LLMs can suppo...

Language models are increasingly deployed in interactive settings where users reason about facts over time rather than in isolation.In such scenarios, correct behavior requires models to maintain and update implicit temporal assumptions established earlier in a conversation. We study this challen...

Large language models (LLMs) are transforming natural language processing across diverse linguistic communities. However, they can reproduce and amplify toxic content, including hate speech, harassment, and bias, posing significant risks to multilingual applications. We provide the first comprehe...