# LongBench

### Paper

Title: `LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding`

Abstract: `In this paper, we introduce LongBench, the first bilingual, multi-task benchmark for long context understanding, enabling a more rigorous evaluation of long context understanding. LongBench comprises 21 datasets across 6 task categories in both English and Chinese, with an average length of 6,711 words (English) and 13,386 characters (Chinese). These tasks cover key long-text application areas including single-doc QA, multi-doc QA, summarization, few-shot learning, synthetic tasks, and code completion. All datasets in LongBench are standardized into a unified format, allowing for effortless automatic evaluation of LLMs`

Homepage: `https://github.com/THUDM/LongBench`


### Citation

```
@inproceedings{bai2024longbench,
    title = "{L}ong{B}ench: A Bilingual, Multitask Benchmark for Long Context Understanding",
    author = "Bai, Yushi and Lv, Xin  and Zhang, Jiajie  and Lyu, Hongchang  and
      Tang, Jiankai  and Huang, Zhidian  and Du, Zhengxiao  and Liu, Xiao  and Zeng, Aohan  and Hou, Lei  and Dong, Yuxiao  and Tang, Jie  and Li, Juanzi",
    booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.acl-long.172",
    doi = "10.18653/v1/2024.acl-long.172",
    pages = "3119--3137",
}
```
> [!NOTE]
> The original implementation suggest not to use `chat_template` for these tasks for instruct models (with add_bos_token=True but model dependent):
> - longbench_fewshot
>    - longbench_trec
>    - longbench_triviaqa
>    - longbench_samsum
>    - longbench_lsht
> - longbench_code
>   - longbench_lcc
>   - longbench_repobench-p


### Groups, Tags, and Tasks

#### Groups

* `longbench_single`: Single-Document QA tasks requiring comprehension of individual documents
* `longbench_multi`: Multi-Document QA tasks requiring information synthesis across multiple documents
* `longbench_summarization`: Summarization tasks for long documents and conversations
* `longbench_fewshot`: Few-shot learning tasks with in-context examples
* `longbench_synthetic`: Synthetic tasks including passage retrieval and counting
* `longbench_code`: Code completion tasks for long code contexts

#### Tags

* `LongBench`: `Benchmark with 21 tasks (avg. 5k-15k tokens) for evaluating long-context capabilities`
* `LongBench-E`: `Modified version with uniform length distribution (0-4k, 4k-8k, 8k+) for analyzing performance across different input lengths`

#### Tasks

* `2wikimqa`: `Question answering task using multiple Wikipedia articles as reference`
* `2wikimqa_e`: `Extended version of 2wikimqa with additional complexity or data`
* `dureader`: `Chinese machine reading comprehension dataset with real-world queries`
* `gov_report`: `Summarization task for long government reports and documents`
* `gov_report_e`: `Extended version of gov_report with additional complexity or data`
* `hotpotqa`: `Multi-hop question answering requiring reasoning across multiple paragraphs`
* `hotpotqa_e`: `Extended version of hotpotqa with additional complexity or data`
* `lcc`: `Long-form content classification across various categories and domains`
* `lcc_e`: `Extended version of lcc with additional complexity or data`
* `lsht`: `Large-scale hierarchical text classification task`
* `multi_news`: `Multi-document news summarization task`
* `multi_news_e`: `Extended version of multi_news with additional complexity or data`
* `multifieldqa_en`: `English question answering across multiple knowledge domains or fields`
* `multifieldqa_en_e`: `Extended version of multifieldqa_en with additional complexity or data`
* `multifieldqa_zh`: `Chinese question answering across multiple knowledge domains or fields`
* `musique`: `Multi-step reasoning question answering with complex queries`
* `narrativeqa`: `Question answering based on book and movie narratives`
* `passage_count`: `Task requiring counting or quantifying information across passages`
* `passage_count_e`: `Extended version of passage_count with additional complexity or data`
* `passage_retrieval_en`: `English passage retrieval task for information seeking`
* `passage_retrieval_en_e`: `Extended version of passage_retrieval_en with additional complexity or data`
* `passage_retrieval_zh`: `Chinese passage retrieval task for information seeking`
* `qasper`: `Question answering on scientific papers requiring domain knowledge`
* `qasper_e`: `Extended version of qasper with additional complexity or data`
* `qmsum`: `Query-based meeting summarization task`
* `repobench-p`: `Programming task based on code repositories`
* `repobench-p_e`: `Extended version of repobench-p with additional complexity or data`
* `samsum`: `Dialogue summarization for messenger-like conversations`
* `samsum_e`: `Extended version of samsum with additional complexity or data`
* `trec`: `Question classification task for information retrieval`
* `trec_e`: `Extended version of trec with additional complexity or data`
* `triviaqa`: `Large-scale question answering dataset with trivia questions`
* `triviaqa_e`: `Extended version of triviaqa with additional complexity or data`
* `vcsum`: `Video conference summarization task`

### Checklist

For adding novel benchmarks/datasets to the library:
* [x] Is the task an existing benchmark in the literature?
  * [x] Have you referenced the original paper that introduced the task?
  * [x] If yes, does the original paper provide a reference implementation? If so, have you checked against the reference implementation and documented how to run such a test?


If other tasks on this dataset are already supported:
* [x] Is the "Main" variant of this task clearly denoted?
* [x] Have you provided a short sentence in a README on what each new variant adds / evaluates?
* [x] Have you noted which, if any, published evaluation setups are matched by this variant?

### Changelog
v2.: fix doc_to_target; add vcsum

v3: properly use all answers for metric calculation; trim whitespace from resps; fix stop sequences not parsing correctly.

v4: fixed special characters in prompts; use greedy decoding by default.