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arXiv · 2504.07646

On the Temporal Question-Answering Capabilities of Large Language Models Over Anonymized Data

Alfredo Garrachón Ruiz·Tomás de la Rosa·Daniel Borrajo
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Abstract

The applicability of Large Language Models (LLMs) in temporal reasoning tasks over data that is not present during training is still a field that remains to be explored. In this paper we work on this topic, focusing on structured and semi-structured anonymized data. We not only develop a direct LLM pipeline, but also compare various methodologies and conduct an in-depth analysis. We identified and examined seventeen common temporal reasoning tasks in natural language, focusing on their algorithmic components. To assess LLM performance, we created the \textit{Reasoning and Answering Temporal Ability} dataset (RATA), featuring semi-structured anonymized data to ensure reliance on reasoning rather than on prior knowledge. We compared several methodologies, involving SoTA techniques such as Tree-of-Thought, self-reflexion and code execution, tuned specifically for this scenario. Our results suggest that achieving scalable and reliable solutions requires more than just standalone LLMs, highlighting the need for integrated approaches.

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Alfredo Garrachón Ruiz, Tomás de la Rosa, Daniel Borrajo. 2025-04-10. On the Temporal Question-Answering Capabilities of Large Language Models Over Anonymized Data. https://arxiv.org/abs/2504.07646

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