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README.md

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+---
+license: apache-2.0
+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
+---
+
+<div align="center">
+  <h1>
+    <img src="images/logo.png" width="40" style="vertical-align: -30%;" alt="LongBench-Pro Logo"/> 
+    LongBench-Pro: A More Realistic and Comprehensive Bilingual Long-Context Evaluation Benchmark
+  </h1>
+</div>
+
+<div align="center">
+
+[![Github Repo](https://img.shields.io/badge/Github-Repo-blue?logo=github&logoColor=white)]() &nbsp;&nbsp;
+[![Leaderboard](https://img.shields.io/badge/🏆-Leaderboard-red)]() &nbsp;&nbsp;
+[![Paper](https://img.shields.io/badge/📄-Arxiv_Paper-green)]()
+
+</div>
+
+---
+
+**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.
+
+<div align="center">
+  <img src="images/bench_comparison.png" width="100%"/>
+</div>
+
+## 🧩 Task Framework
+
+<div align="center">
+  <img src="images/task_definition.png" width="100%"/>
+  <br />
+  <br />
+  <img src="images/task_map.png" width="80%"/>
+  <br />
+  <b>Task mapping between LongBench Pro and existing benchmarks</b>
+</div>
+
+## 📊 Dataset Statistics
+
+<div align="center">
+  <img src="images/sample_distrubution.png" width="100%"/>
+</div>
+
+## 📝 Data Format
+
+**LongBench Pro** organizes data in the following format:
+
+```json
+{
+    "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:
+
+```python
+from datasets import load_dataset
+dataset = load_dataset('caskcsg/LongBench_Pro', split='train')
+```
+
+### Evaluation
+
+Please refer to our [Github Repo]() for automated evaluation.
+
+## 📖 Citation
+
+*Coming Soon...*

longbench_pro.json

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