Datasets:
Tasks:
Question Answering
Modalities:
Text
Formats:
parquet
Languages:
English
Size:
10K - 100K
License:
metadata
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
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
- 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.
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
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
@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.
