DateLogicQA
DateLogicQA, created by the University of Aberdeen, is a benchmark dataset for evaluating large language models' temporal reasoning abilities. It contains 190 questions covering various date formats, temporal contexts, and reasoning types. The dataset is designed to test models' understanding and inference of dates across past, present, and future contexts, especially handling diverse date formats and preserving semantic meaning. It enables researchers to analyze LLM performance on temporal reasoning tasks and identify biases related to time data, with applications in event planning, historical QA, and other scenarios requiring precise temporal inference.
Dataset description and usage context
Temporal Bias Analysis in Large Language Models
DateLogicQA Dataset
Overview
The DateLogicQA dataset aims to explore how large language models (LLMs) handle dates presented in different formats and contexts. It includes 190 questions divided into four categories: commonsense, factual, conceptual, and numeric. Each question uses one of seven date formats and spans three temporal contexts: past, present, and future. This systematic variation enables in‑depth analysis of LLM performance on temporal information.
Examples
- Numeric: What date is 7 years and 9 months after year 27101446?
- Factual: Which person died in the year 23041616? A) Shah Jahan B) Miguel de Cervantes C) Princess Diana D) William Shakespeare
- Conceptual: The first iPhone was released on 29062007. How many years have passed since its release?
- Commonsense: John was born on 15‑03‑1985. He graduated from university on 01‑05‑2007. Was John older than 18 at graduation?
Date Formats
- DDMMYYYY: 23041616
- MMDDYYYY: 04231616
- DDMonYYYY: 23April1616
- DD‑MM‑YY: 23-04-16
- YYYY, Mon DD: 1616, April 23
- DD/YYYY (Julian calendar): 113/1616
- YYYY/DD (Julian calendar): 1616/113
Dataset Access
The DateLogicQA dataset is available on Hugging Face at: https://huggingface.co/datasets/gagan3012/DateLogicQA.
Pair the dataset with AI analysis and content workflows.
Once the source passes your review, move straight into summarization, transformation, report drafting, or presentation generation with the JuheAI toolchain.