Inferring Events from Time Series using Language Models
Mingtian Tan University of Virginia, Charlottesville, Mike A Merrill Stanford University, Zachary Gottesman University of Virginia, Charlottesville, Tim Althoff Department of Computer Science, University of Washington, David Evans University of Virginia, Thomas Hartvigsen University of Virginia, Charlottesville
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 reason about events associated with time series data based on sports data, and develop a new benchmarking method. In experiments spanning 18 LLMs, we prompt LLMs to infer unobserved events given time series data and observe surprising successes, even when providing minimal context. We then show that combining distillation with Reinforcement Learning (RL) can improve the performance for small language models to approach that of large proprietary reasoning models. All resources needed to reproduce our work are available: https://github.com/hartvigsen-group/GAMETime.
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Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表