From Existence to Exhaustiveness: Unveiling the Compounding Failures of LLMs in Multi-answer Event Temporal Reasoning

Abstract

Large Language Models (LLMs) have achieved remarkable success in temporal reasoning. However, existing benchmarks predominantly adopt a "single-answer" paradigm, focusing on verifying the existence of a specific fact while overlooking the challenge of exhaustiveness. In real-world scenarios, entities often simultaneously play multiple roles or exist in multiple states within the same timeframe. To bridge this gap, we introduce MulTR, a comprehensive benchmark designed for multi-answer temporal reasoning from long unstructured contexts. Specifically, MulTR integrates structured temporal facts from Wikidata and natural language text from Wikipedia MulTR integrates structured temporal facts from Wikidata and natural language text from Wikipedia through a logic-driven synthesis process. Notably, we formulate two distinct settings, question-dependent and document-dependent, based on the presence of cue words in the question. It is designed to systematically decouple temporal reasoning capabilities from the uncertainty of the number of answers. Experiment results demonstrate that state-of-the-art models suffer from retrieval laziness, terminating the search process prematurely after locating the first valid piece of evidence. Consequently, their performance drops sharply when evaluated on strict exact match metrics. MulTR, as a diagnostic testing platform, reveal these defects and establish the rigorous standard for future research in dynamic knowledge processing. The MulTR benchmark and evaluation prompt are publicly available at https://github.com/TemporalNLP/MulTR.