--- 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)