license: apache-2.0
configs:
- config_name: default
data_files:
- split: test
path: longbench_pro.json
task_categories:
- question-answering
- text-classification
- table-question-answering
- summarization
language:
- en
- zh
tags:
- Long Context
- Realistic
- Comprehensive
pretty_name: LongBench Pro
size_categories:
- 1K<n<10K
LongBench Pro: A More Realistic and Comprehensive Bilingual Long-Context Evaluation Benchmark
LongBench-Pro, containing 1,500 samples, is entirely built on authentic, natural long documents and includes 11 primary tasks and 25 secondary tasks, covering all long-context capabilities assessed by existing benchmarks. It employs diverse evaluation metrics, enabling a more fine-grained measurement of model abilities, and provides a balanced set of bilingual samples in both English and Chinese.
In addition, LongBench Pro introduces a multi-dimensional taxonomy to support a comprehensive evaluation of models under different operating conditions:
- Context Requirement: Full context (global integration) versus Partial context (localized retrieval);
- Length: Six lengths uniformly distributed from 8k to 256k tokens, used to analyze scaling behavior;
- Difficulty: Four levels ranging from Easy to Extreme, defined based on model performance.
🧩 Task Framework
Task mapping between LongBench Pro and existing benchmarks
📊 Dataset Statistics
📝 Data Format
LongBench Pro organizes data in the following format:
{
"id": "Sample ID: unique for each sample.",
"context": "Long context: 14 types of texts covering domains such as news, medicine, science, literature, law, and education, with various forms such as reports, tables, code, dialogues, lists, and JSON.",
"language": "Sample language: English or Chinese.",
"token_length": "Sample token length: 8k, 16k, 32k, 64k, 128k, or 256k (calculated using the Qwen tokenizer)",
"primary_task": "Primary task type: 11 types.",
"secondary_task": "Secondary task type: 25 types.",
"contextual_requirement": "Contextual Requirement: Full or Partial.",
"question_nonthinking": "Non-thinking prompt of the question: direct answer required.",
"question_thinking": "Thinking prompt of the question: think first, then answer.",
"answer": ["List of components that constitute the answer."],
"difficulty": "Sample difficulty: Easy, Moderate, Hard or Extreme."
}
🧰 How to use it?
Loading Data
You can download and load LongBench Pro data using the following code:
from datasets import load_dataset
dataset = load_dataset('caskcsg/LongBench-Pro', split='test')
Evaluation
Please refer to our Github Repo for automated evaluation.
📖 Citation
Coming Soon...