Datasets:
File size: 30,343 Bytes
175e6ae f61f6c2 175e6ae f61f6c2 29ee647 f61f6c2 29ee647 47ac8bb 72ce14e cc4e964 ad68a11 92b6c5f b8e164b daa2c2c e42ab2e ff8e5ca eaa16f1 95083b3 850fa26 01117d5 f662137 b7eb05f a9ece0f 1ba139b eb4961b b1c571e 9726770 08db517 54c203e bfdf754 679e6f7 b61c7c7 f61f6c2 29ee647 47ac8bb 72ce14e cc4e964 ad68a11 92b6c5f b8e164b daa2c2c e42ab2e ff8e5ca eaa16f1 95083b3 850fa26 01117d5 f662137 b7eb05f a9ece0f 1ba139b eb4961b b1c571e 9726770 08db517 54c203e bfdf754 679e6f7 b61c7c7 175e6ae 7956e94 175e6ae 10e175d 175e6ae 7956e94 175e6ae c2a61c1 7956e94 c2a61c1 175e6ae 7956e94 175e6ae 7956e94 175e6ae 7956e94 175e6ae 7956e94 175e6ae c2a61c1 7956e94 c2a61c1 7956e94 c2a61c1 7956e94 c2a61c1 7956e94 175e6ae c2a61c1 175e6ae 7956e94 175e6ae c2a61c1 7956e94 175e6ae 7956e94 175e6ae 7956e94 c2a61c1 175e6ae c2a61c1 175e6ae 7956e94 c2a61c1 7956e94 c2a61c1 7956e94 c2a61c1 7956e94 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 |
---
language:
- en
- zh
size_categories:
- 1K<n<10K
task_categories:
- question-answering
- text-generation
- summarization
- conversational
- text-classification
tags:
- Long Context
dataset_info:
- config_name: 2wikimqa
features:
- name: input
dtype: string
- name: context
dtype: string
- name: answers
list: string
- name: length
dtype: int32
- name: dataset
dtype: string
- name: language
dtype: string
- name: all_classes
list: string
- name: _id
dtype: string
splits:
- name: test
num_bytes: 5982997
num_examples: 200
download_size: 3595131
dataset_size: 5982997
- config_name: 2wikimqa_e
features:
- name: input
dtype: string
- name: context
dtype: string
- name: answers
list: string
- name: length
dtype: int32
- name: dataset
dtype: string
- name: language
dtype: string
- name: all_classes
list: string
- name: _id
dtype: string
splits:
- name: test
num_bytes: 11331920
num_examples: 300
download_size: 6782587
dataset_size: 11331920
- config_name: dureader
features:
- name: input
dtype: string
- name: context
dtype: string
- name: answers
list: string
- name: length
dtype: int32
- name: dataset
dtype: string
- name: language
dtype: string
- name: all_classes
list: string
- name: _id
dtype: string
splits:
- name: test
num_bytes: 8212951
num_examples: 200
download_size: 5167177
dataset_size: 8212951
- config_name: gov_report
features:
- name: input
dtype: string
- name: context
dtype: string
- name: answers
list: string
- name: length
dtype: int32
- name: dataset
dtype: string
- name: language
dtype: string
- name: all_classes
list: string
- name: _id
dtype: string
splits:
- name: test
num_bytes: 11593569
num_examples: 200
download_size: 5504355
dataset_size: 11593569
- config_name: gov_report_e
features:
- name: input
dtype: string
- name: context
dtype: string
- name: answers
list: string
- name: length
dtype: int32
- name: dataset
dtype: string
- name: language
dtype: string
- name: all_classes
list: string
- name: _id
dtype: string
splits:
- name: test
num_bytes: 14263436
num_examples: 300
download_size: 6669354
dataset_size: 14263436
- config_name: hotpotqa
features:
- name: input
dtype: string
- name: context
dtype: string
- name: answers
list: string
- name: length
dtype: int32
- name: dataset
dtype: string
- name: language
dtype: string
- name: all_classes
list: string
- name: _id
dtype: string
splits:
- name: test
num_bytes: 11379153
num_examples: 200
download_size: 6626936
dataset_size: 11379153
- config_name: hotpotqa_e
features:
- name: input
dtype: string
- name: context
dtype: string
- name: answers
list: string
- name: length
dtype: int32
- name: dataset
dtype: string
- name: language
dtype: string
- name: all_classes
list: string
- name: _id
dtype: string
splits:
- name: test
num_bytes: 12324268
num_examples: 300
download_size: 7196922
dataset_size: 12324268
- config_name: lcc
features:
- name: input
dtype: string
- name: context
dtype: string
- name: answers
list: string
- name: length
dtype: int32
- name: dataset
dtype: string
- name: language
dtype: string
- name: all_classes
list: string
- name: _id
dtype: string
splits:
- name: test
num_bytes: 6878988
num_examples: 500
download_size: 2348393
dataset_size: 6878988
- config_name: lcc_e
features:
- name: input
dtype: string
- name: context
dtype: string
- name: answers
list: string
- name: length
dtype: int32
- name: dataset
dtype: string
- name: language
dtype: string
- name: all_classes
list: string
- name: _id
dtype: string
splits:
- name: test
num_bytes: 17755543
num_examples: 300
download_size: 5530346
dataset_size: 17755543
- config_name: lsht
features:
- name: input
dtype: string
- name: context
dtype: string
- name: answers
list: string
- name: length
dtype: int32
- name: dataset
dtype: string
- name: language
dtype: string
- name: all_classes
list: string
- name: _id
dtype: string
splits:
- name: test
num_bytes: 13005634
num_examples: 200
download_size: 8143066
dataset_size: 13005634
- config_name: multi_news
features:
- name: input
dtype: string
- name: context
dtype: string
- name: answers
list: string
- name: length
dtype: int32
- name: dataset
dtype: string
- name: language
dtype: string
- name: all_classes
list: string
- name: _id
dtype: string
splits:
- name: test
num_bytes: 2715969
num_examples: 200
download_size: 1501391
dataset_size: 2715969
- config_name: multi_news_e
features:
- name: input
dtype: string
- name: context
dtype: string
- name: answers
list: string
- name: length
dtype: int32
- name: dataset
dtype: string
- name: language
dtype: string
- name: all_classes
list: string
- name: _id
dtype: string
splits:
- name: test
num_bytes: 11308405
num_examples: 294
download_size: 5833166
dataset_size: 11308405
- config_name: multifieldqa_en
features:
- name: input
dtype: string
- name: context
dtype: string
- name: answers
list: string
- name: length
dtype: int32
- name: dataset
dtype: string
- name: language
dtype: string
- name: all_classes
list: string
- name: _id
dtype: string
splits:
- name: test
num_bytes: 4427988
num_examples: 150
download_size: 1850093
dataset_size: 4427988
- config_name: multifieldqa_en_e
features:
- name: input
dtype: string
- name: context
dtype: string
- name: answers
list: string
- name: length
dtype: int32
- name: dataset
dtype: string
- name: language
dtype: string
- name: all_classes
list: string
- name: _id
dtype: string
splits:
- name: test
num_bytes: 4428288
num_examples: 150
download_size: 1829910
dataset_size: 4428288
- config_name: multifieldqa_zh
features:
- name: input
dtype: string
- name: context
dtype: string
- name: answers
list: string
- name: length
dtype: int32
- name: dataset
dtype: string
- name: language
dtype: string
- name: all_classes
list: string
- name: _id
dtype: string
splits:
- name: test
num_bytes: 3541307
num_examples: 200
download_size: 1447281
dataset_size: 3541307
- config_name: musique
features:
- name: input
dtype: string
- name: context
dtype: string
- name: answers
list: string
- name: length
dtype: int32
- name: dataset
dtype: string
- name: language
dtype: string
- name: all_classes
list: string
- name: _id
dtype: string
splits:
- name: test
num_bytes: 13965034
num_examples: 200
download_size: 8130878
dataset_size: 13965034
- config_name: narrativeqa
features:
- name: input
dtype: string
- name: context
dtype: string
- name: answers
list: string
- name: length
dtype: int32
- name: dataset
dtype: string
- name: language
dtype: string
- name: all_classes
list: string
- name: _id
dtype: string
splits:
- name: test
num_bytes: 21682299
num_examples: 200
download_size: 1308980
dataset_size: 21682299
- config_name: passage_count
features:
- name: input
dtype: string
- name: context
dtype: string
- name: answers
list: string
- name: length
dtype: int32
- name: dataset
dtype: string
- name: language
dtype: string
- name: all_classes
list: string
- name: _id
dtype: string
splits:
- name: test
num_bytes: 13417957
num_examples: 200
download_size: 4953911
dataset_size: 13417957
- config_name: passage_count_e
features:
- name: input
dtype: string
- name: context
dtype: string
- name: answers
list: string
- name: length
dtype: int32
- name: dataset
dtype: string
- name: language
dtype: string
- name: all_classes
list: string
- name: _id
dtype: string
splits:
- name: test
num_bytes: 11123592
num_examples: 300
download_size: 3868032
dataset_size: 11123592
- config_name: passage_retrieval_en
features:
- name: input
dtype: string
- name: context
dtype: string
- name: answers
list: string
- name: length
dtype: int32
- name: dataset
dtype: string
- name: language
dtype: string
- name: all_classes
list: string
- name: _id
dtype: string
splits:
- name: test
num_bytes: 11234534
num_examples: 200
download_size: 7041865
dataset_size: 11234534
- config_name: passage_retrieval_en_e
features:
- name: input
dtype: string
- name: context
dtype: string
- name: answers
list: string
- name: length
dtype: int32
- name: dataset
dtype: string
- name: language
dtype: string
- name: all_classes
list: string
- name: _id
dtype: string
splits:
- name: test
num_bytes: 11108473
num_examples: 300
download_size: 6962499
dataset_size: 11108473
- config_name: passage_retrieval_zh
features:
- name: input
dtype: string
- name: context
dtype: string
- name: answers
list: string
- name: length
dtype: int32
- name: dataset
dtype: string
- name: language
dtype: string
- name: all_classes
list: string
- name: _id
dtype: string
splits:
- name: test
num_bytes: 3660028
num_examples: 200
download_size: 2683824
dataset_size: 3660028
- config_name: qasper
features:
- name: input
dtype: string
- name: context
dtype: string
- name: answers
list: string
- name: length
dtype: int32
- name: dataset
dtype: string
- name: language
dtype: string
- name: all_classes
list: string
- name: _id
dtype: string
splits:
- name: test
num_bytes: 4792612
num_examples: 200
download_size: 1869662
dataset_size: 4792612
- config_name: qasper_e
features:
- name: input
dtype: string
- name: context
dtype: string
- name: answers
list: string
- name: length
dtype: int32
- name: dataset
dtype: string
- name: language
dtype: string
- name: all_classes
list: string
- name: _id
dtype: string
splits:
- name: test
num_bytes: 6856180
num_examples: 224
download_size: 2012933
dataset_size: 6856180
- config_name: qmsum
features:
- name: input
dtype: string
- name: context
dtype: string
- name: answers
list: string
- name: length
dtype: int32
- name: dataset
dtype: string
- name: language
dtype: string
- name: all_classes
list: string
- name: _id
dtype: string
splits:
- name: test
num_bytes: 11619927
num_examples: 200
download_size: 973894
dataset_size: 11619927
- config_name: repobench-p
features:
- name: input
dtype: string
- name: context
dtype: string
- name: answers
list: string
- name: length
dtype: int32
- name: dataset
dtype: string
- name: language
dtype: string
- name: all_classes
list: string
- name: _id
dtype: string
splits:
- name: test
num_bytes: 24155458
num_examples: 500
download_size: 7757199
dataset_size: 24155458
- config_name: repobench-p_e
features:
- name: input
dtype: string
- name: context
dtype: string
- name: answers
list: string
- name: length
dtype: int32
- name: dataset
dtype: string
- name: language
dtype: string
- name: all_classes
list: string
- name: _id
dtype: string
splits:
- name: test
num_bytes: 20378217
num_examples: 300
download_size: 6633193
dataset_size: 20378217
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-*
- config_name: passage_count
data_files:
- split: test
path: passage_count/test-*
- config_name: passage_count_e
data_files:
- split: test
path: passage_count_e/test-*
- config_name: passage_retrieval_en
data_files:
- split: test
path: passage_retrieval_en/test-*
- config_name: passage_retrieval_en_e
data_files:
- split: test
path: passage_retrieval_en_e/test-*
- config_name: passage_retrieval_zh
data_files:
- split: test
path: passage_retrieval_zh/test-*
- config_name: qasper
data_files:
- split: test
path: qasper/test-*
- config_name: qasper_e
data_files:
- split: test
path: qasper_e/test-*
- config_name: qmsum
data_files:
- split: test
path: qmsum/test-*
- config_name: repobench-p
data_files:
- split: test
path: repobench-p/test-*
- config_name: repobench-p_e
data_files:
- split: test
path: repobench-p_e/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](task.md). 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
```python
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
```python
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](https://huggingface.co/datasets/THUDM/LongBench/resolve/main/data.zip) to load the data.
#### Data Format
All data in **LongBench** (LongBench-E) are standardized to the following format:
```json
{
"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](https://github.com/THUDM/LongBench).
# 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](https://hotpotqa.github.io/), [2WikiMultihopQA](https://aclanthology.org/2020.coling-main.580/), [MuSiQue](https://arxiv.org/abs/2108.00573), and [DuReader](https://github.com/baidu/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](https://arxiv.org/pdf/1712.07040.pdf), [Qasper](https://arxiv.org/pdf/2105.03011.pdf), [GovReport](https://arxiv.org/pdf/2104.02112.pdf), [QMSum](https://arxiv.org/pdf/2104.05938.pdf) and [MultiNews](https://aclanthology.org/P19-1102.pdf) directly use the data provided by the original papers. In the specific construction, we use the template provided by [ZeroSCROLLS](https://www.zero.scrolls-benchmark.com/) to convert the corresponding data into pure text input.
- The [VCSUM](https://arxiv.org/abs/2305.05280) 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](https://nlp.cs.washington.edu/triviaqa/) task is constructed in the manner of [CoLT5](https://arxiv.org/abs/2303.09752), 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](https://aclanthology.org/D19-5409.pdf), [TREC](https://aclanthology.org/C02-1150.pdf) and [LSHT](http://tcci.ccf.org.cn/conference/2014/dldoc/evatask6.pdf) 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](https://arxiv.org/abs/1910.10683). 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](https://arxiv.org/abs/2306.14893) task, we sample from the original code completion dataset. In the [RepoBench-P](https://arxiv.org/abs/2306.03091) 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}
}
``` |