ChronoBias / README.md
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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

Paper GitHub License

Dataset Description

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

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


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.