Datasets:
File size: 12,579 Bytes
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---
license: mit
task_categories:
- question-answering
- translation
- summarization
- text-classification
- text-retrieval
language:
- en
- zh
tags:
- Long Context
size_categories:
- 1K<n<10K
configs:
- config_name: mnds-news_semantic-multiple
data_files:
- split: test
path: classification/mnds-news_semantic-multiple.jsonl
- config_name: thucnews_explicit-single
data_files:
- split: test
path: classification/thucnews_explicit-single.jsonl
- config_name: mnds-news_explicit-multiple
data_files:
- split: test
path: classification/mnds-news_explicit-multiple.jsonl
- config_name: thucnews_explicit-multiple
data_files:
- split: test
path: classification/thucnews_explicit-multiple.jsonl
- config_name: mnds-news_explicit-single
data_files:
- split: test
path: classification/mnds-news_explicit-single.jsonl
- config_name: bigpatent_global_cls
data_files:
- split: test
path: classification/bigpatent_global_cls.jsonl
- config_name: marc
data_files:
- split: test
path: classification/marc.jsonl
- config_name: thucnews_semantic-multiple
data_files:
- split: test
path: classification/thucnews_semantic-multiple.jsonl
- config_name: online-shopping
data_files:
- split: test
path: classification/online-shopping.jsonl
- config_name: wikitext-103
data_files:
- split: test
path: nli/wikitext-103.jsonl
- config_name: wiki2019zh
data_files:
- split: test
path: nli/wiki2019zh.jsonl
- config_name: tedtalks-zh2en
data_files:
- split: test
path: translation/tedtalks-zh2en.jsonl
- config_name: news-commentary-zh2en
data_files:
- split: test
path: translation/news-commentary-zh2en.jsonl
- config_name: open-subtitles-zh2en
data_files:
- split: test
path: translation/open-subtitles-zh2en.jsonl
- config_name: open-subtitles-en2zh
data_files:
- split: test
path: translation/open-subtitles-en2zh.jsonl
- config_name: news-commentary-en2zh
data_files:
- split: test
path: translation/news-commentary-en2zh.jsonl
- config_name: tedtalks-en2zh
data_files:
- split: test
path: translation/tedtalks-en2zh.jsonl
- config_name: cnnnews
data_files:
- split: test
path: summarization/cnnnews.jsonl
- config_name: clts
data_files:
- split: test
path: summarization/clts.jsonl
- config_name: cnewsum
data_files:
- split: test
path: summarization/cnewsum.jsonl
- config_name: booksum
data_files:
- split: test
path: summarization/booksum.jsonl
- config_name: cepsum
data_files:
- split: test
path: summarization/cepsum.jsonl
- config_name: pubmed
data_files:
- split: test
path: summarization/pubmed.jsonl
- config_name: lcsts
data_files:
- split: test
path: summarization/lcsts.jsonl
- config_name: news2016
data_files:
- split: test
path: summarization/news2016.jsonl
- config_name: arxiv
data_files:
- split: test
path: summarization/arxiv.jsonl
- config_name: wikihow
data_files:
- split: test
path: summarization/wikihow.jsonl
- config_name: bigpatent_global_sum
data_files:
- split: test
path: summarization/bigpatent_global_sum.jsonl
- config_name: ncls
data_files:
- split: test
path: summarization/ncls.jsonl
- config_name: drcd_semantic-single
data_files:
- split: test
path: qa/drcd_semantic-single.jsonl
- config_name: duorc
data_files:
- split: test
path: qa/duorc.jsonl
- config_name: nq-open
data_files:
- split: test
path: qa/nq-open.jsonl
- config_name: newsqa
data_files:
- split: test
path: qa/newsqa.jsonl
- config_name: triviaqa
data_files:
- split: test
path: qa/triviaqa.jsonl
- config_name: c3
data_files:
- split: test
path: qa/c3.jsonl
- config_name: dureader
data_files:
- split: test
path: qa/dureader.jsonl
- config_name: hotpotqa
data_files:
- split: test
path: qa/hotpotqa.jsonl
- config_name: wow
data_files:
- split: test
path: topic_retrieval/wow.jsonl
- config_name: drcd_explicit-single
data_files:
- split: test
path: topic_retrieval/drcd_explicit-single.jsonl
---
## Introduction
**M4LE** is a **M**ulti-ability, **M**ulti-range, **M**ulti-task, bilingual benchmark for long-context evaluation. We categorize long-context understanding into five distinct abilities by considering whether it is required to identify single or multiple spans in long contexts based on explicit or semantic hints. Specifically, these abilities are explicit single-span, semantic single-span, explicit multiple-span, semantic multiple-span, and global. Different from previous long-context benchmark that simply compile from a set of existing long NLP benchmarks, we introduce an automated method to transform short-sequence tasks into a comprehensive long-sequence scenario encompassing all these capabilities.
M4LE consists of 36 tasks, covering 11 task types and 12 domains. For each task, we construct 200 instances for each context length bucket (1K, 2K, 4K, 6K, 8K, 12K, 16K, 24K, 32K). Due to computation and cost constraints, our paper evaluated 11 well-established LLMs on instances up to the 8K context length bucket. For more details, please refer to the paper available at <https://arxiv.org/abs/2310.19240>. You can also explore the Github page at <https://github.com/KwanWaiChung/M4LE>.
## Usage
You can load the dataset by specifying the task name:
```python
from datasets import load_dataset
tasks = [
"arxiv",
"bigpatent_global_cls",
"bigpatent_global_sum",
"booksum",
"c3",
"cepsum",
"clts+",
"cnewsum",
"cnnnews",
"drcd_explicit-single",
"drcd_semantic-single",
"duorc",
"dureader",
"hotpotqa",
"lcsts",
"marc",
"mnds-news_explicit-single",
"mnds-news_explicit-multiple",
"mnds-news_semantic-multiple",
"ncls",
"news-commentary-en2zh",
"news-commentary-zh2en",
"news2016",
"newsqa",
"nq-open",
"online-shopping",
"open-subtitles-en2zh",
"open-subtitles-zh2en",
"pubmed",
"tedtalks-en2zh",
"tedtalks-zh2en",
"thucnews_explicit-single",
"thucnews_explicit-multiple",
"thucnews_semantic-multiple",
"triviaqa",
"wiki2019zh",
"wikihow",
"wikitext-103",
"wow",
]
for task in tasks:
data = load_dataset('wckwan/M4LE', task, split='test')
```
## Format
Each testing instance follows this format:
```yaml
{
"instruction": "<task description>",
"input": "<task input with one-shot example>",
"answers": ["<answer1>", "<answer2>"],
"input_length": <int, number of words in instruction and input separated by space>,
"total_length": <int, number of words in instruction, input and gold answer separated by space>,
"length_bucket": <int, the length bucket to which this instance belongs>
}
```
## Tasks
Here is the full list for the tasks with their descriptions. More details about these tasks, please refer to the paper .
Ability | Task Name | Task Type | Language | Description
----------------- | ------------------------------------------- | ---------- | -------- | ------------------------------------------------------------------
Explicit Single | mnds-news_explicit-single | CLS + RET | En | Classify a specified news article.
Explicit Single | thucnews_explicit-single | CLS + RET | Zh | Classify a specified news article.
Explicit Single | newsqa | QA + RET | En | Answer a question based on a specified news article.
Explicit Single | c3 | QA + RET | Zh | Answer a multi-choice question based on a textbook extract.
Explicit Single | wow | RET | En | Return the ID of the article related to a specified topic.
Explicit Single | drcd_explicit-single | RET | Zh | Return the ID of the article related to a specified topic.
Explicit Single | cnnnews | SUM + RET | En | Summarize a specified news article.
Explicit Single | cepsum | SUM + RET | Zh | Summarize a specified product description.
Explicit Single | lcsts | SUM + RET | Zh | Summarize a specified news article.
Explicit Single | ncls | SUM + RET | En, Zh | Summarize a specified news article.
Explicit Multiple | mnds-news_explicit-multiple | CLS + RET | En | Return the IDs of all the articles belong to a specified class.
Explicit Multiple | thucnews_explicit-multiple | CLS + RET | Zh | Return the IDs of all the articles belong to a specified class.
Explicit Multiple | marc | CLS + RET | En, Zh | Return the IDs of all the positive product reviews.
Explicit Multiple | online-shopping | CLS + RET | Zh | Return the IDs of all the positive product reviews.
Semantic Single | wikitext-103 | NLI + RET | En | Return the ID of the paragraph that continues a query paragraph.
Semantic Single | wiki2019zh | NLI + RET | Zh | Return the ID of the paragraph that continues a query paragraph.
Semantic Single | duorc | QA | En | Answer a question based on multiple movie plots.
Semantic Single | nq-open | QA | En | Answer a question based on multiple wikipedia paragraphs.
Semantic Single | dureader | QA | Zh | Answer a question based on multiple web snippets.
Semantic Single | drcd_semantic-single | QA | Zh | Answer a question based on multiple wikipedia paragraphs.
Semantic Single | wikihow | SUM + RET | En | Summarize an article based on a given topic.
Semantic Single | news2016 | SUM + RET | Zh | Summarize a news article based on a given title.
Semantic Single | tedtalks-en2zh/tedtalks-zh2en | TRAN + RET | En, Zh | Translate a Ted Talk transcript based on a given title.
Semantic Multiple | mnds-news_semantic-multiple | CLS + CNT | En | Return the number of news articles belonging to a specified class.
Semantic Multiple | thucnews_semantic-multiple | CLS + CNT | Zh | Return the number of news articles belonging to a specified class.
Semantic Multiple | hotpotqa | QA | En | Answer a question based on multiple wikipedia paragraphs.
Global | bigpatent_global_cls | CLS | En | Classify a patent document.
Global | triviaqa | QA | En | Answer a question based on a web snippet.
Global | arxiv | SUM | En | Summarize an academic paper.
Global | bigpatent_global_sum | SUM | En | Summarize a patent document.
Global | pubmed | SUM | En | Summarize a medical paper.
Global | booksum | SUM | En | Summarize one or more chapters of a book.
Global | cnewsum | SUM | Zh | Summarize a news article.
Global | clts+ | SUM | Zh | Summarize a news article.
Global | open-subtitles-en2zh/open-subtitles-zh2en | TRAN | En, Zh | Translate the movie subtitles.
Global | news-commentary-en2zh/news-commentary-zh2en | TRAN | En, Zh | Translate the movie subtitles.
## Citation
If you find our paper and resources useful, please consider citing our paper:
```bibtex
@misc{kwan_m4le_2023,
title = {{{M4LE}}: {{A Multi-Ability Multi-Range Multi-Task Multi-Domain Long-Context Evaluation Benchmark}} for {{Large Language Models}}},
author = {Kwan, Wai-Chung and Zeng, Xingshan and Wang, Yufei and Sun, Yusen and Li, Liangyou and Shang, Lifeng and Liu, Qun and Wong, Kam-Fai},
year = {2023},
}
```
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