---
license: cc-by-4.0
task_categories:
- summarization
- text-generation
language:
- en
tags:
- croissant
# viewer: false
size_categories:
- 10K -->
We proposed AcademicEval, a live benchmark for evaluating LLMs over long-context generation tasks. AcademicEval adopts papers on arXiv to introduce several acadeic writing tasks with long-context inputs, i.e., Title, Abstract, Introduction, Related Work, wich covers a wide range of abstraction levels and require no manual labeling.
Comparing to existing long-context LLM benchmarks, our Comparing to existing long-context LLM benchmarks, our AcademicEval offers flexible length, automatic annotation, hierarchical abstraction, few-shot demonstrations, and live updates without data leakage risks.
**🌟Note🌟: currently, for the ease of downloading, we only uploaded the test set of AcademicEval (The rest of AcademicEval, i.e., train and val set, can be accessed via [AcademicEval Full](https://huggingface.co/datasets/ulab-ai/AcademicEval_Full)). The data viewer above shows the preview data information of **title-10K**, **abs-9K**, and **intro-8K**. For the complete test set data, please check "Files and versions" in this page.**
| Benchmark |
Avg Len |
Automatic Annotation |
Hierarchical Abstraction |
Few-shot Demonstrations |
Live Update |
| ZeroSCROLLS (Shaham et al., 2023) |
~10K |
✓ |
✘ |
✘ |
✘ |
| L-Eval (An et al., 2023) |
~8K |
✘ |
✘ |
✘ |
✘ |
| BAMBOO (Dong et al., 2023) |
~16K |
✘ |
✘ |
✘ |
✘ |
| LongBench (Bai et al., 2023) |
~8K |
✘ |
✘ |
✓ |
✘ |
| LooGLE (Li et al., 2023) |
~20K |
✘ |
✘ |
✘ |
✘ |
| ∞Bench (Zhang et al., 2024) |
~200K |
✘ |
✘ |
✘ |
✘ |
| AcademicEval (ours) |
Flexible |
✓ |
✓ |
✓ |
✓ |
# **Dataset Structure**
## Data Settings
- ***Title Writing***
- **title_10K**
- **title_30K**
- **title_31K_G**
- ***Abstract Writing***
- **abs_9K**
- **abs_28K**
- **abs_29K_G**
- ***Introduction Writing***
- **intro_8K**
- **intro_28K**
- **intro_28K_G**
- ***Related Work Writing***
- **related_34K**
- **related_53K**
- **related_53K_G**
## Main Data Fields
+ **url:** the url of the original paper on arXiv
+ **title:** the title of the paper
+ **abstract:** the abstract of the paper
+ **authors:** the authors of the paper
+ **published:** the publication timestamp of the paper
+ **primary_cat:** arXiv category
+ **gt:** the ground truth of the corresponding task
+ **main_content:** the main body of the paper (w/o the corresponding section content)
+ **additional_info:** the few-shot demonstrations from randomly selected papers (the data fields of each demonstration are the same as above)
+ **additional_graph_info:** the few-shot demonstrations with the co-author subgraph structure from co-author papers (the data fields of each demonstration are the same as above)