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

Source: [On the Temporal Question-Answering Capabilities of Large Language Models Over Anonymized Data](<https://www.sota2.com/research/paper/68fa77f01e209131cea76235>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-12-04T05:01:31.288Z

Authors: Alfredo Garrach\\'on Ruiz, Tom\\'as de la Rosa, Daniel Borrajo

Published: 2025-04-10T10:48:42.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2504.07646>)

[PDF](<https://arxiv.org/pdf/2504.07646.pdf>)