Test of Time: A Benchmark for Evaluating LLMs on Temporal Reasoning

TL;DR

This paper introduces synthetic datasets and an evaluation framework to systematically assess large language models' temporal reasoning abilities, addressing limitations of real-world data and providing insights into model performance on complex temporal tasks.

Contribution

The work presents novel synthetic datasets and an open-source evaluation framework specifically designed for testing LLMs on temporal reasoning, enabling controlled and comprehensive analysis.

Findings

Identified strengths and weaknesses of LLMs in temporal reasoning

Analyzed the impact of question structure and size on performance

Provided datasets for future benchmarking and research

Abstract

Large language models (LLMs) have showcased remarkable reasoning capabilities, yet they remain susceptible to errors, particularly in temporal reasoning tasks involving complex temporal logic. Existing research has explored LLM performance on temporal reasoning using diverse datasets and benchmarks. However, these studies often rely on real-world data that LLMs may have encountered during pre-training or employ anonymization techniques that can inadvertently introduce factual inconsistencies. In this work, we address these limitations by introducing novel synthetic datasets specifically designed to assess LLM temporal reasoning abilities in various scenarios. The diversity of question types across these datasets enables systematic investigation into the impact of the problem structure, size, question type, fact order, and other factors on LLM performance. Our findings provide valuable insights into the strengths and weaknesses of current LLMs in temporal reasoning tasks. To foster further research in this area, we are open-sourcing the datasets and evaluation framework used in our experiments: https://huggingface.co/datasets/baharef/ToT.