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README.md

@@ -93,4 +93,59 @@ configs:
   data_files:
   - split: test
     path: "L-CiteEval-Hardness/counting_stars.json"
----
+---
+
+# L-CITEEVAL: DO LONG-CONTEXT MODELS TRULY LEVERAGE CONTEXT FOR RESPONDING?
+**Paper** [![arXiv](https://img.shields.io/badge/arXiv-2410.02115-b31b1b.svg?style=plastic)](https://arxiv.org/abs/2410.02115) &nbsp; **Github** <a href="https://github.com/ZetangForward/L-CITEEVAL"><img src="https://github.githubassets.com/assets/GitHub-Mark-ea2971cee799.png" alt="Github" width="40" height="40"></a> &nbsp;**Zhihu** [![Zhihu](https://img.shields.io/badge/知乎-0079FF.svg?style=plastic&logo=zhihu&logoColor=white)](https://zhuanlan.zhihu.com/p/817442176)
+
+## Benchmark Quickview
+*L-CiteEval* is a multi-task long-context understanding with citation benchmark, covering **5 task categories**, including single-document question answering, multi-document question answering, summarization, dialogue understanding, and synthetic tasks, encompassing **11 different long-context tasks**. The context lengths for these tasks range from **8K to 48K**.
+![](assets/dataset.png)
+
+## Data Prepare
+#### Load Data
+```
+from datasets import load_dataset
+
+datasets = ["narrativeqa", "natural_questions", "hotpotqa", "2wikimultihopqa", "goc_report", "multi_news", "qmsum", "locomo", "dialsim", "counting_stars", "niah"]
+
+for dataset in datasets:
+    ### Load L-CiteEval
+    data = load_dataset('Jonaszky123/L-CiteEval', f"L-CiteEval-Data_{dataset}")
+
+    ### Load L-CiteEval-Length
+    data = load_dataset('Jonaszky123/L-CiteEval', f"L-CiteEval-Length_{dataset}")
+
+    ### Load L-CiteEval-Hardness
+    data = load_dataset('Jonaszky123/L-CiteEval', f"L-CiteEval-Hardness_{dataset}")
+
+```
+
+<!-- You can get the L-CiteEval data from [🤗 Hugging face](). Once downloaded, place the data in the dataset folder. -->
+
+All data in L-CiteEval follows the format below:
+```
+{
+    "id": "The identifier for the data entry",
+    "question": "The task question, such as for single-document QA. In summarization tasks, this may be omitted",
+    "answer": "The correct or expected answer to the question, used for evaluating correctness",
+    "docs": "Context divided into fixed-length chunks"
+    "length": "The context length"
+    "hardness": "The level of diffulty in L-CiteEval-Hardness, which can be easy, medium and hard"
+}
+```
+
+You can find evaluation code in our github.
+
+## Citation
+If you find our work helpful, please cite our paper:
+```
+@misc{tang2024lciteeval,
+    title={L-CiteEval: Do Long-Context Models Truly Leverage Context for Responding?},
+    author={Zecheng Tang and Keyan Zhou and Juntao Li and Baibei Ji and Jianye Hou and Min Zhang},
+    year={2024},
+    eprint={2410.02115},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```