Datasets:

wckwan
/

M4LE

	---
	license: mit
	task_categories:
	- question-answering
	- translation
	- summarization
	- text-classification
	- text-retrieval
	language:
	- en
	- zh
	tags:
	- Long Context
	size_categories:
	- 1K<n<10K
	---

	## Introduction

	M4LE is a Multi-ability, Multi-range, Multi-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},
	}
	```