Datasets:

SCUT-DLVCLab
/

WenMind

	---
	license: cc-by-nc-sa-4.0
	task_categories:
	- question-answering
	language:
	- zh
	pretty_name: WenMind Benchmark
	---
	# WenMind Benchmark

	NOTE this README was copied from https://github.com/SCUT-DLVCLab/WenMind/blob/main/README.md

	- 2024/09/26 WenMind Benchmark paper has been accepted by NeurIPS 2024.

	WenMind is a comprehensive benchmark dedicated for evaluating Large Language Models (LLMs) in Chinese Classical Literature and Language Arts (CCLLA). WenMind covers the sub-domains of Ancient Prose, Ancient Poetry, and Ancient Literary Culture, comprising 4,875 question-answer pairs, spanning 42 fine-grained tasks (as shown in the figure 1), 3 question formats (Fill-in-the-Blank questions, Multiple-Choice questions and Question-and-Answer questions), and 2 evaluation scenarios (domain-oriented and capability-oriented).

	<p align="center">
	<img src="https://github.com/SCUT-DLVCLab/WenMind/blob/main/Images/WenMind_Overall.png?raw=true" width="800"/>
	<p>

	<p align="center">
	<strong>Figure 1: Overview of WenMind Benchmark, which covers 3 sub-domains and 42 fine-gained tasks.</strong>
	<p>

	## Download

	You can obtain the complete WenMind evaluation dataset from WenMind Benchmark folder on GitHub.

	## Data Format
	```
	{
	"id": 2464,
	"domain": "ancient literary culture",
	"capability": "knowledge",
	"question_format": "QA",
	"coarse_grained_task_zh": "成语",
	"coarse_grained_task_en": "idiom",
	"fine_grained_task_zh": "成语解释",
	"fine_grained_task_en": "idiom explanation",
	"question": "解释下面成语的意思：\n暮去朝来",
	"answer": "黄昏过去，清晨又到来。形容时光流逝。"
	}
	```

	The following is an explanation of the various fields in the data samples:

	- `id`: The unique identifier for the data sample, used to distinguish different samples.

	- `domain`: The domain to which the data sample belongs, including ancient prose, ancient poetry and ancient literary culture.

	- `capability`: The type of capability of the data sample, including knowledge, understanding and generation.

	- `question_format`: The format of the question, indicating the type of question in the sample, including FB, MCQ and QA.

	- `coarse_grained_task_zh`: The Chinese name of the coarse-grained task classification. Describes the coarse-grained task category of the sample, with a total of 26 categories.

	- `coarse_grained_task_en`: The English name of the coarse-grained task classification. Corresponds to `coarse_grained_task_zh`, describing the coarse-grained task category of the sample, with a total of 26 categories.

	- `fine_grained_task_zh`: The Chinese name of the fine-grained task classification. Describes the fine-grained task category of the sample, with a total of 42 categories.

	- `fine_grained_task_en`: The English name of the fine-grained task classification. Corresponds to `fine_grained_task_zh`, describing the fine-grained task category of the sample, with a total of 42 categories.

	- `question`: The actual content of the question. The question to be answered in the sample.

	- `answer`: The answer to the corresponding question. Provides a detailed response to the question.

	## Task List

	### T1-1: Inverted Sentence Structure (倒装句语序)
	- Task Description: Correct word order for inverted sentences.
	- Capability: Understanding
	- Scale: 18

	### T1-2: Elliptical Sentence (省略句)
	- Task Description: Answer the omitted information in the elliptical sentence.
	- Capability: Understanding
	- Scale: 32

	### T1-3: Inverted Sentence Types (倒装句类型)
	- Task Description: Identify the inversion type of inverted sentences.
	- Capability: Understanding
	- Scale: 7

	### T1-4: Sentence Structure Identification (判断句式)
	- Task Description: Identify the sentence's syntactic type.
	- Capability: Understanding
	- Scale: 43

	### T2: Classical Chinese to Modern Chinese (文白翻译)
	- Task Description: Translate classical Chinese into modern Chinese.
	- Capability: Understanding
	- Scale: 200

	### T3: Modern Chinese to Classical Chinese (白文翻译)
	- Task Description: Translate modern Chinese into classical Chinese.
	- Capability: Understanding
	- Scale: 200

	### T4: Named Entity Recognition (命名实体识别)
	- Task Description: Extract named entities from Classical Chinese sentences.
	- Capability: Understanding
	- Scale: 200

	### T5: Punctuation (句读)
	- Task Description: Add punctuation to Classical Chinese sentences.
	- Capability: Understanding
	- Scale: 200

	### T6: Topic Classification (主题分类)
	- Task Description: Select theme categories based on Classical Chinese sentences.
	- Capability: Understanding
	- Scale: 200

	### T7: Word Explanation (字词解释)
	- Task Description: Explain the words and phrases in Classical Chinese sentences.
	- Capability: Understanding
	- Scale: 100

	### T8: Reading Comprehension (阅读理解)
	- Task Description: Read Classical Chinese texts and answer related questions.
	- Capability: Understanding
	- Scale: 100

	### T9: Function Words (虚词)
	- Task Description: Answer the usage of function words in classical Chinese sentences.
	- Capability: Understanding
	- Scale: 100

	### T10: Homophones (通假字)
	- Task Description: Identify whether a character is a homophone.
	- Capability: Understanding
	- Scale: 200

	### T11: Polysemy (单字多义)
	- Task Description: Distinguish between different meanings of the same character.
	- Capability: Understanding
	- Scale: 200

	### T12: Classical Chinese Writing (文言文写作)
	- Task Description: Writing in classical Chinese.
	- Capability: Generation
	- Scale: 100

	### T13-1: Appreciation Exam Questions (赏析真题)
	- Task Description: Answer appreciation questions based on ancient poetry.
	- Capability: Understanding
	- Scale: 150

	### T13-2: Free Appreciation (自由赏析)
	- Task Description: Conduct a free and detailed analysis of ancient poetry.
	- Capability: Understanding
	- Scale: 100

	### T14-1: Poetry Writing (诗创作)
	- Task Description: Compose a poem based on the theme.
	- Capability: Generation
	- Scale: 30

	### T14-2: Ci Writing (词创作)
	- Task Description: Compose a ci based on the theme.
	- Capability: Generation
	- Scale: 50

	### T14-3: Qu Writing (曲创作)
	- Task Description: Compose a qu based on the theme.
	- Capability: Generation
	- Scale: 20

	### T15-1: Content Q&A (内容问答)
	- Task Description: Answer the complete content of ancient poetry according to the title and author.
	- Capability: Knowledge
	- Scale: 200

	### T15-2: Title and Author Q&A (题目作者问答)
	- Task Description: Answer the title and author according to the content of ancient poetry.
	- Capability: Knowledge
	- Scale: 200

	### T15-3: Write the Next Sentence (下句默写)
	- Task Description: Write the next sentence according to the previous sentence in the ancient poem.
	- Capability: Knowledge
	- Scale: 100

	### T15-4: Write the Previous Sentence (上句默写)
	- Task Description: Write the previous sentence according to the next sentence in the ancient poem.
	- Capability: Knowledge
	- Scale: 100

	### T15-5: Comprehension Dictation (理解性默写)
	- Task Description: Provide ancient poetry sentences that meet the requirements.
	- Capability: Knowledge
	- Scale: 30

	### T15-6: Genre Judgment (判断体裁)
	- Task Description: Judge the genre of ancient poetry.
	- Capability: Knowledge
	- Scale: 120

	### T16: Ancient Poetry Translation (古诗词翻译)
	- Task Description: Translate ancient poetry into modern Chinese.
	- Capability: Understanding
	- Scale: 200

	### T17: Sentiment Classification (情感分类)
	- Task Description: Judge the sentiment contained in ancient poetry.
	- Capability: Understanding
	- Scale: 200

	### T18: Ancient Poetry to English (古诗词英文翻译)
	- Task Description: Translate ancient poetry into English.
	- Capability: Understanding
	- Scale: 50

	### T19: Poet Introduction (诗人介绍)
	- Task Description: Provide a detailed introduction of the poet.
	- Capability: Knowledge
	- Scale: 110

	### T20: Analysis of Imagery (意象解析)
	- Task Description: Provide the meanings of the imagery.
	- Capability: Knowledge
	- Scale: 185

	### T21-1: Couplet Following (接下联)
	- Task Description: Create the following couplet based on the previous one.
	- Capability: Generation
	- Scale: 100

	### T21-2: Couplet Writing (主题创作)
	- Task Description: Write a couplet based on the theme.
	- Capability: Generation
	- Scale: 100

	### T21-3: HengPi Writing (拟横批)
	- Task Description: Write HengPi based on the content of a couplet.
	- Capability: Generation
	- Scale: 100

	### T22-1: Synonyms (近义词)
	- Task Description: Provide the synonym for the idiom.
	- Capability: Knowledge
	- Scale: 100

	### T22-2: The Origin of Idiom (成语出处)
	- Task Description: Provide the source of the idiom.
	- Capability: Knowledge
	- Scale: 100

	### T22-3: Idiom Finding (成语蕴含)
	- Task Description: Extract idioms from ancient Chinese sentences and provide their meanings.
	- Capability: Knowledge
	- Scale: 100

	### T22-4: Idiom Explanation (解释含义)
	- Task Description: Provide the meaning of idioms.
	- Capability: Knowledge
	- Scale: 100

	### T23: Riddle (谜语)
	- Task Description: Guess the answer based on clues or clever hints.
	- Capability: Knowledge
	- Scale: 100

	### T24: Xiehouyu (歇后语)
	- Task Description: Complete the second half of the proverb based on the first half.
	- Capability: Knowledge
	- Scale: 100

	### T25: Historical Chinese Phonology (古汉语音韵)
	- Task Description: Answer questions about ancient Chinese phonetics and rhymes.
	- Capability: Knowledge
	- Scale: 100

	### T26: Knowledge of Sinology Q&A (国学常识问答)
	- Task Description: Answer questions about Sinology.
	- Capability: Knowledge
	- Scale: 130

	## Data Construction

	The construction pipeline of WenMind includes data collection and data processing, as illustrated in Figure 2.

	<p align="center">
	<img src="https://github.com/SCUT-DLVCLab/WenMind/blob/main/Images/Data_Construction.png?raw=true" width="550"/>
	<p>

	<p align="center">
	<strong>Figure 2: Construction pipeline of WenMind Benchmark.</strong>
	<p>

	## Data Statistics

	Table 1 provides the statistics of the WenMind dataset.

	<div align="center">

	Table 1: The statistics of the WenMind Benchmark. "Q" represents "Question" and "A" represents "Answer".

	<table>
	<thead>
	<tr>
	<th align="left"><strong>Domain</strong></th>
	<th align="center"><strong>Tasks</strong></th>
	<th align="center"><strong>#Q</strong></th>
	<th align="center"><strong>Max. #Q</strong></th>
	<th align="center"><strong>Min. #Q</strong></th>
	<th align="center"><strong>Avg. Q Tokens</strong></th>
	<th align="center"><strong>Avg. A Tokens</strong></th>
	</tr>
	</thead>
	<tbody>
	<tr>
	<td align="left">Ancient Prose</td>
	<td align="center">15</td>
	<td align="center">1,900</td>
	<td align="center">200</td>
	<td align="center">7</td>
	<td align="center">107.51</td>
	<td align="center">62.12</td>
	</tr>
	<tr>
	<td align="left">Ancient Poetry</td>
	<td align="center">16</td>
	<td align="center">1,845</td>
	<td align="center">200</td>
	<td align="center">20</td>
	<td align="center">73.42</td>
	<td align="center">94.93</td>
	</tr>
	<tr>
	<td align="left">Ancient Literary Culture</td>
	<td align="center">11</td>
	<td align="center">1,130</td>
	<td align="center">100</td>
	<td align="center">100</td>
	<td align="center">26.68</td>
	<td align="center">14.26</td>
	</tr>
	<tr>
	<td align="left"><strong>Overall</strong></td>
	<td align="center">42</td>
	<td align="center">4,875</td>
	<td align="center">200</td>
	<td align="center">7</td>
	<td align="center">75.87</td>
	<td align="center">63.44</td>
	</tr>
	</tbody>
	</table>

	</div>

	## Inference

	### a. Obtain the model’s responses

	#### Open-source Model

	For open-source models, we perform inference locally, only requiring the model path and the output file path for the answers.

	```
	--model_path The path to the model, defaults to loading from huggingface
	--output_path The file path for the model's answer output, defaults to {model_name}_result.json
	```

	e.g.

	```
	CUDA_VISIBLE_DEVICES=0,1 python Evaluation_Code/Inference/Test_Baichuan2-7B-Chat.py \
	--model_path baichuan-inc/Baichuan2-7B-Chat \
	--output_path Baichuan2-7B-Chat_result.json
	```

	#### API Model

	For GPT-3.5 and GPT-4 models, provide two parameters: `api_base` and `api_key`.
	For ERNIE-3.5 and ERNIE-4.0 models, provide two parameters: `api_key` and `secret_key`.
	For Spark models, provide three parameters: `api_key`, `secret_key`, and `appid`.
	Refer to the official documentation of each API model for details.

	e.g.

	```
	python Test_ERNIE-3.5-8K-0329.py \
	--API_KEY {api_key} \
	--SECRET_KEY {secret_key} \
	--output_path {output_path}
	```
	### b. Use ERNIE-3.5 to score the responses

	Step 1: Check whether the LLM response file is consistent with the format of the `JSON/LLM_Response_Examples.json` file.

	Step 2: Open the `Evaluation_Code/LLM_Scoring.py` file, input the `API_KEY` and `SECRET_KEY` for the scoring model ERNIE-3.5, replace `LLM_response_path` with the storage path of the LLM response file, replace `LLM_score_path` with the path where the scoring results will be saved, and replace `LLM_prompt_path` with the storage path of `JSON/Task_Score_Prompt.json`.

	Step 3: Run the following command to obtain the scoring results:

	```
	python Evaluation_Code/LLM_Scoring.py
	```

	### c. Calculate the model’s score

	Step 1: Check whether the scoring file is consistent with the format of the `JSON/LLM_Score_Examples.json` file.

	Step 2: Open the `Evaluation_Code/Calculate_Score.py` file and replace `LLM_score_path` with the storage path of the scoring file.

	Step 3: Run the following command to obtain the model's score:

	```
	python Evaluation_Code/Calculate_Score.py
	```
	## Evaluation Result

	<p align="center">
	<strong>Table 2: Results of all evaluated models on different domains and capabilities.</strong>
	<p>
	<p align="center">
	<img src="https://github.com/SCUT-DLVCLab/WenMind/blob/main/Images/Evaluation_Result.png?raw=true" width="750"/>
	<p>


	## Acknowledgement

	- [SCUT-C2MChn](https://github.com/Zongyuan-Jiang/C2MChn)
	- [WYWEB](https://github.com/baudzhou/WYWEB)
	- [Daizhige](https://github.com/garychowcmu/daizhigev20)
	- [ACLUE](https://github.com/isen-zhang/ACLUE)
	- [Websites-A Related to Ancient Poetry](http://ts300.5156edu.com/)
	- [Websites-B Related to Ancient Poetry](https://www.gushixuexi.com/)
	- [Sou Yun](https://www.sou-yun.cn/)
	- [THU-FSPC](https://github.com/THUNLP-AIPoet/Datasets)
	- [Han Dian](https://www.zdic.net/)

	## License

	![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)

	The work is licensed under a [MIT License](https://lbesson.mit-license.org/).

	![License: CC BY-NC-SA 4.0](https://img.shields.io/badge/License-CC%20BY--NC--SA%204.0-lightgrey.svg)

	The WenMind benchmark is licensed under a [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-nc-sa/4.0/).