TRAVELER: A Benchmark for Evaluating Temporal Reasoning across Vague, Implicit and Explicit References
This work addresses a gap in systematic evaluation for temporal reasoning in NLP, providing a benchmark for researchers and practitioners, though it is incremental as it builds on existing QA paradigms and datasets.
The authors tackled the problem of evaluating temporal reasoning in natural language understanding by introducing TRAVELER, a synthetic benchmark dataset with 3,300 questions that tests models on explicit, implicit, and vague temporal references, finding that state-of-the-art LLMs perform well on explicit references but deteriorate with larger event sets and less explicit references, with vague questions showing the lowest performance.
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