TRAVELER: A Benchmark for Evaluating Temporal Reasoning across Vague, Implicit and Explicit References

TL;DR

TRAVELER is a new synthetic benchmark dataset designed to evaluate language models' ability to understand and resolve various types of temporal references, including explicit, implicit, and vague, across different event set sizes.

Contribution

It introduces TRAVELER, a comprehensive benchmark for systematic evaluation of temporal reasoning in language models, covering diverse temporal reference types and event set complexities.

Findings

LLMs perform well on explicit references with small event sets

Performance drops as event set size increases and references become less explicit

Vague temporal references pose the greatest challenge for current models

Abstract

Understanding and resolving temporal references is essential in Natural Language Understanding as we often refer to the past or future in daily communication. Although existing benchmarks address a system's ability to reason about and resolve temporal references, systematic evaluation of specific temporal references remains limited. Towards closing this gap, we introduce TRAVELER, a novel synthetic benchmark dataset that follows a Question Answering paradigm and consists of questions involving temporal references with the corresponding correct answers. TRAVELER assesses models' abilities to resolve explicit, implicit relative to speech time, and vague temporal references. Beyond investigating the performance of state-of-the-art LLMs depending on the type of temporal reference, our benchmark also allows evaluation of performance in relation to the length of the set of events. For the category of vague temporal references, ground-truth answers were established via human surveys on Prolific, following a procedure similar to the one from Kenneweg et al. To demonstrate the benchmark's applicability, we evaluate four state-of-the-art LLMs using a question-answering task encompassing 3,300 questions. Our findings show that while the benchmarked LLMs can answer questions over event sets with a handful of events and explicit temporal references successfully, performance clearly deteriorates with larger event set length and when temporal references get less explicit. Notably, the vague question category exhibits the lowest performance across all models. The benchmark is publicly available at: https://gitlab.ub.uni-bielefeld.de/s.kenneweg/TRAVELER