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Datasets:
THUDM
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LongBench

Tasks:
Question Answering
Text Generation
Summarization
Modalities:
Text
Languages:
English
Chinese
Size:
1K - 10K
ArXiv:
Tags:
Long Context
Libraries:
Datasets
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metadata
language:
  - en
  - zh
size_categories:
  - 1K<n<10K
task_categories:
  - question-answering
  - text-generation
  - summarization
  - conversational
  - text-classification
tags:
  - Long Context
dataset_info:
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      - name: answers
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      - name: _id
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  - config_name: dureader
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  - config_name: gov_report
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      - name: answers
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  - config_name: hotpotqa
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  - config_name: lcc
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  - config_name: lcc_e
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  - config_name: lsht
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  - config_name: multi_news
    features:
      - name: input
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      - name: context
        dtype: string
      - name: answers
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      - name: length
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  - config_name: multi_news_e
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  - config_name: multifieldqa_en
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      - name: context
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      - name: answers
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  - config_name: multifieldqa_en_e
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      - name: context
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      - name: _id
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    splits:
      - name: test
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  - config_name: multifieldqa_zh
    features:
      - name: input
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      - name: context
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      - name: answers
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  - config_name: musique
    features:
      - name: input
        dtype: string
      - name: context
        dtype: string
      - name: answers
        list: string
      - name: length
        dtype: int32
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      - name: language
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  - config_name: narrativeqa
    features:
      - name: input
        dtype: string
      - name: context
        dtype: string
      - name: answers
        list: string
      - name: length
        dtype: int32
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    splits:
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configs:
  - config_name: 2wikimqa
    data_files:
      - split: test
        path: 2wikimqa/test-*
  - config_name: 2wikimqa_e
    data_files:
      - split: test
        path: 2wikimqa_e/test-*
  - config_name: dureader
    data_files:
      - split: test
        path: dureader/test-*
  - config_name: gov_report
    data_files:
      - split: test
        path: gov_report/test-*
  - config_name: gov_report_e
    data_files:
      - split: test
        path: gov_report_e/test-*
  - config_name: hotpotqa
    data_files:
      - split: test
        path: hotpotqa/test-*
  - config_name: hotpotqa_e
    data_files:
      - split: test
        path: hotpotqa_e/test-*
  - config_name: lcc
    data_files:
      - split: test
        path: lcc/test-*
  - config_name: lcc_e
    data_files:
      - split: test
        path: lcc_e/test-*
  - config_name: lsht
    data_files:
      - split: test
        path: lsht/test-*
  - config_name: multi_news
    data_files:
      - split: test
        path: multi_news/test-*
  - config_name: multi_news_e
    data_files:
      - split: test
        path: multi_news_e/test-*
  - config_name: multifieldqa_en
    data_files:
      - split: test
        path: multifieldqa_en/test-*
  - config_name: multifieldqa_en_e
    data_files:
      - split: test
        path: multifieldqa_en_e/test-*
  - config_name: multifieldqa_zh
    data_files:
      - split: test
        path: multifieldqa_zh/test-*
  - config_name: musique
    data_files:
      - split: test
        path: musique/test-*
  - config_name: narrativeqa
    data_files:
      - split: test
        path: narrativeqa/test-*

Introduction

LongBench is the first benchmark for bilingual, multitask, and comprehensive assessment of long context understanding capabilities of large language models. LongBench includes different languages (Chinese and English) to provide a more comprehensive evaluation of the large models' multilingual capabilities on long contexts. In addition, LongBench is composed of six major categories and twenty one different tasks, covering key long-text application scenarios such as single-document QA, multi-document QA, summarization, few-shot learning, synthetic tasks and code completion.

We are fully aware of the potentially high costs involved in the model evaluation process, especially in the context of long context scenarios (such as manual annotation costs or API call costs). Therefore, we adopt a fully automated evaluation method, aimed at measuring and evaluating the model's ability to understand long contexts at the lowest cost.

LongBench includes 14 English tasks, 5 Chinese tasks, and 2 code tasks, with the average length of most tasks ranging from 5k to 15k, and a total of 4,750 test data. For detailed statistics and construction methods of LongBench tasks, please refer here. In addition, we provide LongBench-E, a test set with a more uniform length distribution constructed by uniform sampling, with comparable amounts of data in the 0-4k, 4k-8k, and 8k+ length intervals to provide an analysis of the model's performance variations at different input lengths.

Github Repo for LongBench: https://github.com/THUDM/LongBench Arxiv Paper for LongBench: https://arxiv.org/pdf/2308.14508.pdf

How to use it?

Loading Data 

from datasets import load_dataset

datasets = ["narrativeqa", "qasper", "multifieldqa_en", "multifieldqa_zh", "hotpotqa", "2wikimqa", "musique", \
            "dureader", "gov_report", "qmsum", "multi_news", "vcsum", "trec", "triviaqa", "samsum", "lsht", \
            "passage_count", "passage_retrieval_en", "passage_retrieval_zh", "lcc", "repobench-p"]

for dataset in datasets:
    data = load_dataset('THUDM/LongBench', dataset, split='test')

Similarly, you can load the LongBench-E data

from datasets import load_dataset

datasets = ["qasper", "multifieldqa_en", "hotpotqa", "2wikimqa", "gov_report", "multi_news", "trec", \
            "triviaqa", "samsum", "passage_count", "passage_retrieval_en", "lcc", "repobench-p"]

for dataset in datasets:
    data = load_dataset('THUDM/LongBench', f"{dataset}_e", split='test')

Alternatively, you can download the folder from this link to load the data.

Data Format

All data in LongBench (LongBench-E) are standardized to the following format:

{
    "input": "The input/command for the task, usually short, such as questions in QA, queries in Few-shot tasks, etc",
    "context": "The long context required for the task, such as documents, cross-file code, few-shot examples in Few-shot tasks",
    "answers": "A List of all true answers",
    "length": "Total length of the first three items (counted in characters for Chinese and words for English)",
    "dataset": "The name of the dataset to which this piece of data belongs",
    "language": "The language of this piece of data",
    "all_classes": "All categories in classification tasks, null for non-classification tasks",
    "_id": "Random id for each piece of data"
}

Evaluation

This repository provides data download for LongBench. If you wish to use this dataset for automated evaluation, please refer to our github.

Task statistics

Task Task Type Eval metric Avg len Language #Sample
HotpotQA Multi-doc QA F1 9,151 EN 200
2WikiMultihopQA Multi-doc QA F1 4,887 EN 200
MuSiQue Multi-doc QA F1 11,214 EN 200
DuReader Multi-doc QA Rouge-L 15,768 ZH 200
MultiFieldQA-en Single-doc QA F1 4,559 EN 150
MultiFieldQA-zh Single-doc QA F1 6,701 ZH 200
NarrativeQA Single-doc QA F1 18,409 EN 200
Qasper Single-doc QA F1 3,619 EN 200
GovReport Summarization Rouge-L 8,734 EN 200
QMSum Summarization Rouge-L 10,614 EN 200
MultiNews Summarization Rouge-L 2,113 EN 200
VCSUM Summarization Rouge-L 15,380 ZH 200
TriviaQA Few shot F1 8,209 EN 200
SAMSum Few shot Rouge-L 6,258 EN 200
TREC Few shot Accuracy 5,177 EN 200
LSHT Few shot Accuracy 22,337 ZH 200
PassageRetrieval-en Synthetic Accuracy 9,289 EN 200
PassageCount Synthetic Accuracy 11,141 EN 200
PassageRetrieval-zh Synthetic Accuracy 6,745 ZH 200
LCC Code Edit Sim 1,235 Python/C#/Java 500
RepoBench-P Code Edit Sim 4,206 Python/Java 500

Note: In order to avoid discrepancies caused by different tokenizers, we use the word count (using Python's split function) to calculate the average length of English datasets and code datasets, and use the character count to calculate the average length of Chinese datasets.

Task description

Task Task Description
HotpotQA Answer related questions based on multiple given documents
2WikiMultihopQA Answer related questions based on multiple given documents
MuSiQue Answer related questions based on multiple given documents
DuReader Answer related Chinese questions based on multiple retrieved documents
MultiFieldQA-en Answer English questions based on a long article, which comes from a relatively diverse field
MultiFieldQA-zh Answer Chinese questions based on a long article, which comes from a relatively diverse field
NarrativeQA Answer questions based on stories or scripts, including understanding of important elements such as characters, plots, themes, etc.
Qasper Answer questions based on a NLP research paper, questions proposed and answered by NLP practitioners
GovReport A summarization task that requires summarizing government work reports
MultiNews A multi-doc summarization that requires summarizing over multiple news
QMSum A summarization task that requires summarizing meeting records based on user queries
VCSUM A summarization task that requires summarizing Chinese meeting records
SAMSum A dialogue summarization task, providing several few-shot examples
TriviaQA Single document question answering task, providing several few-shot examples
NQ Single document question answering task, providing several few-shot examples
TREC A classification task that requires categorizing questions, includes 50 categories in total
LSHT A Chinese classification task that requires categorizing news, includes 24 categories in total
PassageRetrieval-en Given 30 English Wikipedia paragraphs, determine which paragraph the given summary corresponds to
PassageCount Determine the total number of different paragraphs in a given repetitive article
PassageRetrieval-zh Given several Chinese paragraphs from the C4 data set, determine which paragraph the given abstract corresponds to
LCC Given a long piece of code, predict the next line of code
RepoBench-P Given code in multiple files within a GitHub repository (including cross-file dependencies), predict the next line of code

Task construction

Note: For all tasks constructed from existing datasets, we use data from the validation or test set of the existing dataset (except for VCSUM).

  • The tasks of HotpotQA, 2WikiMultihopQA, MuSiQue, and DuReader are built based on the original datasets and processed to be suitable for long context evaluation. Specifically, for questions in the validation set, we select the evidence passage that contains the answer and several distracting articles. These articles together with the original question constitute the input of the tasks.
  • The tasks of MultiFiedQA-zh and MultiFieldQA-en consist of long artical data from about 10 sources, including Latex papers, judicial documents, government work reports, and PDF documents indexed by Google. For each long artical, we invite several PhD and master students to annotate, i.e., to ask questions based on the long artical and give the correct answers. To better automate evaluation, we ask the annotators to propose questions with definitive answers as much as possible.
  • The tasks of NarrativeQA, Qasper, GovReport, QMSum and MultiNews directly use the data provided by the original papers. In the specific construction, we use the template provided by ZeroSCROLLS to convert the corresponding data into pure text input.
  • The VCSUM task is built based on the original dataset, and we design a corresponding template to convert the corresponding data into pure text input.
  • The TriviaQA task is constructed in the manner of CoLT5, which provides several examples of question and answering based on documents, and requires the language model to answer related questions based on new documents.
  • The tasks of SAMSum, TREC and LSHT are built based on the original datasets. For each question in the validation set, we sample several data from the training set to form few-shot examples. These examples together with the questions in the validation set constitute the input for this task.
  • The PassageRetrieval-en task is constructed based on English Wikipedia. For each piece of data, we randomly sample 30 paragraphs from English Wikipedia and select one for summarization (using GPT-3.5-Turbo). This task requires the model to give the original paragraph name to which the summary corresponds.
  • The PassageCount task is constructed based on the English wiki. For each piece of data, we randomly sample several passages from English Wikipedia, repeat each paragraph at random several times, and finally shuffle the paragraphs. This task requires the model to determine the total number of different paragraphs in the given context.
  • The PasskeyRetrieval-zh task is constructed based on C4. For each piece of data, we randomly sample several Chinese paragraphs from C4 and select one of them for summarization (using GPT-3.5-Turbo). This task requires the model to give the original paragraph name to which the summary corresponds.
  • For the LCC task, we sample from the original code completion dataset. In the RepoBench-P task, we select the most challenging XF-F (Cross-File-First) setting from the original dataset and refer to the Oracle-Filled scenario in the paper. For each original piece of data, we randomly extract multiple cross-file code snippets, including the gold cross-file code snippet, and concatenate them as input, requiring the model to effectively use cross-file code for completion.

LongBench-E statistics

Task Task Type #data in 0-4k #data in 4-8k #data in 8k+
HotpotQA Multi-doc QA 100 100 100
2WikiMultihopQA Multi-doc QA 100 100 100
MultiFieldQA-en Single-doc QA 67 70 13
Qasper Single-doc QA 100 100 24
GovReport Summarization 100 100 100
MultiNews Summarization 100 100 94
TriviaQA Few shot 100 100 100
SAMSum Few shot 100 100 100
TREC Few shot 100 100 100
PassageRetrieval-en Synthetic 100 100 100
PassageCount Synthetic 100 100 100
LCC Code 100 100 100
RepoBench-P Code 100 100 100

Citation 

@misc{bai2023longbench,
      title={LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding}, 
      author={Yushi Bai and Xin Lv and Jiajie Zhang and Hongchang Lyu and Jiankai Tang and Zhidian Huang and Zhengxiao Du and Xiao Liu and Aohan Zeng and Lei Hou and Yuxiao Dong and Jie Tang and Juanzi Li},
      year={2023},
      eprint={2308.14508},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}