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

• URL: http://arxiv.org/abs/2504.07646v1
• Date: Thu, 10 Apr 2025 10:48:42 GMT
• Title: On the Temporal Question-Answering Capabilities of Large Language Models Over Anonymized Data
• Authors: Alfredo Garrachón Ruiz, Tomás de la Rosa, Daniel Borrajo,
• Abstract summary: 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.<n>In this paper we work on this topic, focusing on structured and semi-structured anonymized data.<n>We identify and examined seventeen common temporal reasoning tasks in natural language, focusing on their algorithmic components.
• Score: 1.2979906794584584
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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