---
license: odc-by
task_categories:
- text-classification
- question-answering
- zero-shot-classification
- text-generation
- text2text-generation
- sentence-similarity
- summarization
- feature-extraction
language:
- en
pretty_name: Tempora
size_categories:
- 1K<n<10K
tags:
- medical
- climate
- art
- music
- legal
- chemistry
- biology
- finance
configs:
- config_name: tempora-0325B
  features:
  - name: id
    dtype: string
  - name: source
    dtype: string
  - name: extracted_content
    dtype: string
  splits:
  - name: train
    num_bytes: 1505882.7022682622
    num_examples: 250
  download_size: 644917
  dataset_size: 1505882.7022682622
- config_name: tempora-0325
  data_files:
  - split: train
    path: tempora-0325/train-*
- config_name: tempora-0325-raw
  data_files:
  - split: train
    path: tempora-0325-raw/train-*
- config_name: tempora-0325B
  data_files:
  - split: train
    path: tempora-0325B/train-*
dataset_info:
- config_name: tempora-0325B
  features:
  - name: id
    dtype: string
  - name: source
    dtype: string
  - name: extracted_content
    dtype: string
  splits:
  - name: train
    num_bytes: 1505882.7022682622
    num_examples: 250
  download_size: 644917
  dataset_size: 1505882.7022682622
- config_name: tempora-0325
  features:
  - name: id
    dtype: string
  - name: source
    dtype: string
  - name: extracted_content
    dtype: string
  splits:
  - name: train
    num_bytes: 25873346.79533116
    num_examples: 5599
  download_size: 15080523
  dataset_size: 25873346.79533116
- config_name: tempora-0325-raw
  features:
  - name: id
    dtype: string
  - name: source
    dtype: string
  - name: raw
    dtype: string
  - name: extracted_content
    dtype: string
  - name: extracted_content_stage_2
    dtype: string
  splits:
  - name: train
    num_bytes: 4193359908
    num_examples: 7368
  download_size: 1108481319
  dataset_size: 4193359908
---


# Tempora 
<p align="center">
  <img src="assets/tempora_logo.jpg" alt="Tempora Logo" width="50%">
</p>

> A contemporary dataset of 7,368 real-world documents published **after March 1, 2025**, curated for testing the temporal grounding of Large Language Models.

## Table of Contents
1. [Usage](#usage)
   - [Loading with `datasets`](#loading-with-datasets)
   - [Dataset Example](#dataset-example)
2. [Dataset Overview](#dataset-overview)
3. [Why a Contemporary Dataset?](#why-a-contemporary-dataset)
4. [Scope & Diversity](#scope--diversity)
5. [Evaluating Parametric vs. Contextual Knowledge](#evaluating-parametric-vs-contextual-knowledge)
6. [Methodological Longevity](#methodological-longevity)
7. [Dataset Structure](#dataset-structure)
   - [Available Configurations](#available-configurations)
   - [Data Fields](#data-fields)
   - [Splits and Statistics](#splits-and-statistics)
8. [Licensing](#licensing)
9. [Citation](#citation)
10. [Acknowledgments](#acknowledgments)

---

## Usage

Below are examples of how to load **Tempora-0325** using the [Hugging Face `datasets` library](https://github.com/huggingface/datasets). Adjust the `config_name` as needed.

### Loading with `datasets`

```python
from datasets import load_dataset

# Load the balanced subset
ds_balanced = load_dataset("sumuks/tempora", name="tempora-0325B", split="train")

# Load the main unbalanced corpus
ds_full = load_dataset("sumuks/tempora", name="tempora-0325", split="train")

# Load the raw version
ds_raw = load_dataset("sumuks/tempora", name="tempora-0325-raw", split="train")
```

### Dataset Example

A sample entry from `tempora-0325` might look like:

```python
{
  'id': 'QChCKP-ecAD',
  'source': 'https://www.theguardian.com/sport/2025/mar/09/france-captain-antoine-dupont-rugby-union-injury',
  'extracted_content': "# Antoine Dupont faces long spell out with ruptured cruciate knee ligaments\nAntoine Dupont, France’s talismanic captain and the player ..."
}
```

---

## Dataset Overview

Recent advances in large language models (LLMs) have highlighted a critical gap in testing temporal and factual grounding: models are often pretrained on massive (and sometimes outdated) corpora, making it difficult to discern whether they rely on newly provided textual evidence or memorize stale facts. **Tempora-0325** addresses this challenge by presenting a set of **7,368 documents** published after **March 1, 2025**, ensuring that the vast majority of pretrained models have not seen this data during training.

<p align="center">
  <img src="assets/content_lengths.png" alt="Distribution of Character Lengths in Tempora-0325" width="60%"><br>
  <em>Figure: Distribution of character lengths within Tempora-0325</em>
</p>

---

## Why a Contemporary Dataset?

When LLMs are prompted with documents containing up-to-date facts, regulations, or events, it becomes crucial to separate genuine, context-grounded outputs from those derived purely from parametric memory. **Tempora-0325** focuses on this objective:

- **Temporal testing**: Provides data published exclusively after March 1, 2025.  
- **Unseen textual evidence**: Ensures that most existing models’ pretraining does not include these documents.  
- **Detection of stale knowledge**: Encourages models to rely on newly provided information—or risk inconsistencies revealing outdated parametric knowledge.

---

## Scope & Diversity

We collected **7,368** publicly available documents from:
- Government and corporate announcements  
- Legal and medical reports  
- Sports updates, news articles, and blogs  
- Miscellaneous informational sites  

Each source was verified to have been published after March 1, 2025, with manual checks to confirm the authenticity of time-sensitive information. Two key subsets are made available:

1. **Unbalanced Full Corpus** (Tempora-0325): Mirrors real-world domain distribution.  
2. **Balanced Subset** (Tempora-0325B): Offers uniform coverage across eight categories (government, corporate, legal, medical, sports, news, blogs, miscellaneous) for controlled experimentation.

---

## Evaluating Parametric vs. Contextual Knowledge

A central motivation behind **Tempora-0325** is enabling deeper analysis into how—or even whether—an LLM updates its internal knowledge states when presented with truly novel or conflicting data. By isolating content never encountered in typical pretraining corpora, the dataset can:

- Test retrieval-augmented generation: Determine if a model is using new evidence from a document or relying on outdated internal parameters.  
- Assess summarization and question generation tasks: See whether newly introduced information is being processed accurately or overshadowed by memorized facts.

---

## Methodological Longevity

While **Tempora-0325** is a snapshot of post March 2025 knowledge, the data collection methodology is **open-sourced** so future variants (e.g., **Tempora-0727**) can be built over time. This systematic refresh ensures the dataset remains novel for the next generation of LLMs, preserving its effectiveness for detecting when models override new information with stale, parametric knowledge.

---

## Dataset Structure

### Available Configurations

This repository offers multiple configurations, each corresponding to different data splits or processing stages:

- **tempora-0325B**  
  - Balanced subset of 250 training documents.  
  - Equal coverage of 8 domains for controlled experiments.  
- **tempora-0325**  
  - The full, unbalanced corpus.  
  - 5,599 training documents.  
- **tempora-0325-raw**  
  - The raw version containing minimal processing for advanced or custom use-cases.  
  - 7,368 total documents.

### Data Fields

Depending on the configuration, you will see some or all of the following fields:

- **id** *(string)*: A unique identifier for each document.  
- **source** *(string)*: The source domain or category (e.g., `legal`, `medical`, `sports`), if available.  
- **raw** *(string)*: Unprocessed text content (available in `tempora-0325-raw` only).  
- **extracted_content** *(string)*: The main processed text from each document.  
- **extracted_content_stage_2** *(string)*: Additional content extraction stage (only in `tempora-0325-raw`).

### Splits and Statistics

| Config                | # Documents | Split | Size (approx.) |
|:----------------------|-----------:|:-----:|---------------:|
| **tempora-0325**     |       5,599 | train | ~25.9 MB       |
| **tempora-0325B**    |         250 | train | ~1.5 MB        |
| **tempora-0325-raw** |       7,368 | train | ~4.19 GB       |

---



## Licensing

This dataset is released under the [**Open Data Commons Attribution License (ODC-By) v1.0**](https://opendatacommons.org/licenses/by/1-0/).  
Use of this dataset is also subject to the terms and conditions laid out by each respective source from which documents were collected.

---

## Citation

If you use **Tempora-0325** in your research or application, please cite:

```
Pending! Please contact the authors!
```

---

## Acknowledgments

Special thanks to all domain experts and contributors who helped verify publication dates and authenticity. By regularly refreshing **Tempora** with new data, we hope to advance the understanding of how modern language models adapt to truly novel, time-sensitive content.

---

*(Last updated: March 17, 2025)*