---
language:
- en
license: mit
task_categories:
- question-answering
tags:
- fairness
- bias
- llm-evaluation
- temporal-bias
- football
- rag
pretty_name: ChronoBias
size_categories:
- 10K<n<100K
---

# ChronoBias

[![Paper](https://img.shields.io/badge/Paper-EMNLP%202025-blue)](https://aclanthology.org/2025.findings-emnlp.405/)
[![GitHub](https://img.shields.io/badge/GitHub-Repository-black)](https://github.com/kmkim95/ChronoBias)
[![License](https://img.shields.io/badge/License-MIT-green)](https://opensource.org/licenses/MIT)

## Dataset Description

**ChronoBias** is a benchmark for evaluating **time-conditional group bias** in the time-sensitive knowledge of Large Language Models (LLMs).

- **Paper:** [ChronoBias: A Benchmark for Evaluating Time-conditional Group Bias in the Time-sensitive Knowledge of Large Language Models](https://aclanthology.org/2025.findings-emnlp.405/)
- **Venue:** Findings of EMNLP 2025
- **Authors:** Kyungmin Kim, Youngbin Choi, Hyounghun Kim, Dongwoo Kim, Sangdon Park

### Overview

The benchmark focuses on two types of bias:

- **Parametric Knowledge Bias**: Bias in group-specific knowledge encoded in model parameters
- **Time-sensitivity Bias**: Bias in the degree of time-sensitivity across groups

The dataset covers football league standings from multiple international leagues, enabling controlled evaluation of LLM performance across groups (leagues) and time periods.

![Benchmark Overview](https://raw.githubusercontent.com/kmkim95/ChronoBias/main/assets/benchmark_vis-1.png)

---

## Dataset Statistics

| Field | Value |
|---|---|
| Total examples | 13,502 |
| Group | 6 (EPL, K-League1, Saudi Pro League, Ligue 1 Algeria, Serie A Brazil, Primera Liga Spain) |
| Time-sensitivity | 3 (never_changing, slow_changing, moderate_to_fast_changing) |

---

## Data Fields

| Field | Type | Description |
|---|---|---|
| `id` | string | Unique identifier for the example |
| `question_id` | string | Identifier for the question |
| `league_name` | string | Name of the football league |
| `season_name` | string | Season year |
| `round` | string | Match round number |
| `question` | string | The question asked to the LLM |
| `answer` | list | Ground-truth answer (list of [team, points] pairs) |
| `context` | string | HTML table with league standings as context (for RAG) |
| `current_date` | string | The date at which the question is posed (YYYY-MM-DD) |
| `start_date` | string | Start date of the round (YYYY/MM/DD) |
| `end_date` | string | End date of the round (YYYY/MM/DD) |
| `end_month` | string | End month of the round (YYYY/MM) |
| `qtype` | string | Question subtype (e.g., top_1, bottom_3) |
| `qtype_agg` | string | Aggregated question type (top or bottom) |
| `question_type` | string | Question category |
| `time_type` | string | How fast this type of information changes over time |

---

## Usage

```python
from datasets import load_dataset

dataset = load_dataset("tml-lab/ChronoBias")
data = dataset["train"]

# Filter by league
epl_data = data.filter(lambda x: x["league_name"] == "EPL")

# Filter by question type
top1_data = data.filter(lambda x: x["qtype"] == "top_1")

# Use context field for RAG evaluation
example = data[0]
print(example["question"])   # Question to the LLM
print(example["context"])    # HTML table with league standings
print(example["answer"])     # Ground-truth answer
```

---

## Citation

```bibtex
@inproceedings{kim-etal-2025-chronobias,
    title = "{C}hrono{B}ias: A Benchmark for Evaluating Time-conditional Group Bias in the Time-sensitive Knowledge of Large Language Models",
    author = "Kim, Kyungmin  and
      Choi, Youngbin  and
      Kim, Hyounghun  and
      Kim, Dongwoo  and
      Park, Sangdon",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.findings-emnlp.405/",
    doi = "10.18653/v1/2025.findings-emnlp.405",
    pages = "7658--7693",
}
```

---

## License

This dataset is released under the [MIT License](https://opensource.org/licenses/MIT).