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https://github.com/wgwang/awesome-LLM-benchmarks

Awesome LLM Benchmarks to evaluate the LLMs across text, code, image, audio, video and more.
https://github.com/wgwang/awesome-LLM-benchmarks

List: awesome-LLM-benchmarks

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Awesome LLM Benchmarks to evaluate the LLMs across text, code, image, audio, video and more.

Host: GitHub
URL: https://github.com/wgwang/awesome-LLM-benchmarks
Owner: wgwang
Created: 2023-12-13T00:59:48.000Z (11 months ago)
Default Branch: main
Last Pushed: 2024-01-03T01:01:52.000Z (10 months ago)
Last Synced: 2024-05-21T22:04:51.983Z (6 months ago)
Size: 1.92 MB
Stars: 70
Watchers: 4
Forks: 6
Open Issues: 1
Metadata Files:
- Readme: README.md

Awesome Lists containing this project

awesome-ai-list-guide - awesome-LLM-benchmarks

README

# 大模型评测数据集和工具大全

Awesome LLM Benchmarks to evaluate the LLMs across text, code, image, audio, video and more.

大模型评测数据集和工具大全，涵盖文本、代码、图像、声音、视频以及跨模态等。

旨在记录大模型评测数据集和工具，欢迎在**Issues**中提供提供**线索**和**素材**

使用数据请注明来源：**微信公众号：走向未来** 和 **仓库：https://github.com/wgwang/awesome-LLM-benchmarks**

Awesome family related to LLMS includes:
- https://github.com/wgwang/awesome-LLM-benchmarks
- https://github.com/wgwang/awesome-LLMs-In-China
- https://github.com/wgwang/awesome-open-foundation-models

大模型相关的Awesome系列包括：
- 大模型评测数据集：
https://github.com/wgwang/awesome-LLM-benchmarks
- 中国大模型列表：
https://github.com/wgwang/awesome-LLMs-In-China
- 开源开放基础大模型列表：
https://github.com/wgwang/awesome-open-foundation-models

微信扫码关注我的微信公众号：**走向未来**，分享有关大模型、AGI、知识图谱、深度学习、强化学习、计算机视觉、自然语言处理等等与人工智能有关的内容。

![](imgs/走向未来.jpg)

**Star一下，举手之劳！**

**下面分类列出评测数据集和工具**

## 事实性 Factuality 评测数据集

### BoolQ

Christopher Clark, Kenton Lee, Ming-Wei Chang, Tom Kwiatkowski, Michael Collins, and Kristina Toutanova. BoolQ: Exploring the surprising difficulty of natural yes/no questions. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 2924–2936, 2019. URL https://aclanthology.org/N19-1300.

### NaturalQuestions-Closed & NaturalQuestions-Retrieved

Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, Kristina Toutanova, Llion Jones, Matthew Kelcey, Ming-Wei Chang, Andrew M. Dai, Jakob Uszkoreit, Quoc Le, and Slav Petrov. Natural questions: A benchmark for question answering research. Transactions of the Association for Computational Linguistics, 7:452–466, 2019. doi: 10.1162/tacl_a_00276. URL https:// aclanthology.org/Q19-1026.

### RealtimeQA

Jungo Kasai, Keisuke Sakaguchi, Yoichi Takahashi, Ronan Le Bras, Akari Asai, Xinyan Yu, Dragomir Radev, Noah A. Smith, Yejin Choi, and Kentaro Inui. RealTime QA: What’s the answer right now?, 2022. URL https://arxiv.org/abs/2207.13332.

### TydiQA-noContext 和 TydiQA-goldP

Jon Clark, Eunsol Choi, Michael Collins, Dan Garrette, Tom Kwiatkowski, Vitaly Nikolaev, and Jennimaria Palomaki. TydiQA: A benchmark for information-seeking question answering in typo- logically diverse languages. Transactions of the Association for Computational Linguistics, 2020. URL https://storage.googleapis.com/tydiqa/tydiqa.pdf.

### CSQA

Saha, Amrita, Vardaan Pahuja, Mitesh Khapra, Karthik Sankaranarayanan, and Sarath Chandar. Complex sequential question answering: Towards learning to converse over linked question answer pairs with a knowledge graph. In Proceedings of the AAAI conference on artificial intelligence, vol. 32, no. 1. 2018.

## 长上下文 Long Context 评测数据集

### NarrativeQA

Tomáš Kočiský, Jonathan Schwarz, Phil Blunsom, Chris Dyer, Karl Moritz Hermann, Gábor Melis, and Edward Grefenstette. The NarrativeQA reading comprehension challenge. Transactions of the Association for Computational Linguistics, 6:317–328, 2018. doi: 10.1162/tacl_a_00023. URL https://aclanthology.org/Q18-1023.

### Scrolls-Qasper和Scrolls-Quality

Uri Shaham, Elad Segal, Maor Ivgi, Avia Efrat, Ori Yoran, Adi Haviv, Ankit Gupta, Wenhan Xiong, Mor Geva, Jonathan Berant, and Omer Levy. SCROLLS: Standardized CompaRison over long language sequences. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 12007–12021, Abu Dhabi, United Arab Emirates, December 2022. Association for Computational Linguistics. URL https://aclanthology.org/2022.emnlp-main.823.

### XLsum

Tahmid Hasan, Abhik Bhattacharjee, Md. Saiful Islam, Kazi Mubasshir, Yuan-Fang Li, Yong-Bin Kang, M. Sohel Rahman, and Rifat Shahriyar. XL-sum: Large-scale multilingual abstractive summarization for 44 languages. In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pages 4693–4703, Online, August 2021. Association for Computational Linguistics. doi: 10.18653/ v1/2021.findings-acl.413. URL https://aclanthology.org/2021.findings-acl.413.

## 数学/科学Math/Science 评测数据集

### GSM8k

Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Jacob Hilton, Reiichiro Nakano, Christopher Hesse, and John Schulman. Training verifiers to solve math word problems. arXiv preprint arXiv:2110.14168, 2021. URL https://arxiv.org/abs/2110.14168.

### MATH

Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, and Jacob Steinhardt. Measuring mathematical problem solving with the MATH dataset. arXiv preprint arXiv:2103.03874, 2021b. URL https://arxiv.org/abs/2103.03874.

### MMLU

Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. Measuring massive multitask language understanding. Proceedings of the International Conference on Learning Representations (ICLR), 2021a.

### Math-StackExchange（EXEQ-300k and OFEQ-10k）
Yuan, Ke, Dafang He, Zhuoren Jiang, Liangcai Gao, Zhi Tang, and C. Lee Giles. Automatic generation of headlines for online math questions. In Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, no. 05, pp. 9490-9497. 2020.

### GaoKao-Benchmark

Zhang, Xiaotian, Chunyang Li, Yi Zong, Zhengyu Ying, Liang He, and Xipeng Qiu. Evaluating the Performance of Large Language Models on GAOKAO Benchmark. arXiv preprint arXiv:2305.12474 (2023).

### C-eval
Huang, Yuzhen, Yuzhuo Bai, Zhihao Zhu, Junlei Zhang, Jinghan Zhang, Tangjun Su, Junteng Liu et al. "C-eval: A multi-level multi-discipline chinese evaluation suite for foundation models." arXiv preprint arXiv:2305.08322 (2023).

## 推理Reasoning 评测数据集

### BigBench

Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, Abu Awal Md Shoeb, Abubakar Abid, Adam Fisch, Adam R. Brown, et al. Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615, 2022. URL https://arxiv.org/abs/ 2206.04615.

### CLRS

Petar Veličković, Adrià Puigdomènech Badia, David Budden, Razvan Pascanu, Andrea Banino, Misha Dashevskiy, Raia Hadsell, and Charles Blundell. The clrs algorithmic reasoning benchmark. arXiv preprint arXiv:2205.15659, 2022.

### Proof Writer

Oyvind Tafjord, Bhavana Dalvi, and Peter Clark. Proof Writer: Generating implications, proofs, and abductive statements over natural language. In Findings, 2020. URL https://api. semanticscholar.org/CorpusID:229371222.

### Reasoning-Fermi problems 推理-费米问题

Ashwin Kalyan, Abhinav Kumar, Arjun Chandrasekaran, Ashish Sabharwal, and Peter Clark. How much coffee was consumed during emnlp 2019? fermi problems: A new reasoning challenge for ai, 2021.

### Lambada

Denis Paperno, Germán Kruszewski, Angeliki Lazaridou, Quan Ngoc Pham, Raffaella Bernardi, Sandro Pezzelle, Marco Baroni, Gemma Boleda, and Raquel Fernández. The LAMBADA dataset: Word prediction requiring a broad discourse context. arXiv preprint arXiv:1606.06031, 2016.

### HellaSwag

Rowan Zellers, Ari Holtzman, Yonatan Bisk, Ali Farhadi, and Yejin Choi. Hellaswag: Can a machine really finish your sentence? arXiv preprint arXiv:1905.07830, 2019.

### DROP

Dheeru Dua, Yizhong Wang, Pradeep Dasigi, Gabriel Stanovsky, Sameer Singh, and Matt Gardner. DROP: A reading comprehension benchmark requiring discrete reasoning over paragraphs. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 2368–2378, 2019. URL https://aclanthology.org/N19-1246.

### PIQA

Bisk, Yonatan, Rowan Zellers, Jianfeng Gao, and Yejin Choi. Piqa: Reasoning about physical commonsense in natural language. In Proceedings of the AAAI conference on artificial intelligence, vol. 34, no. 05, pp. 7432-7439. 2020.

## 摘要Summarization 评测数据集

### XL Sum

### WikiLingua

Faisal Ladhak, Esin Durmus, Claire Cardie, and Kathleen McKeown. WikiLingua: A new benchmark dataset for cross-lingual abstractive summarization. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 4034–4048, Online, November 2020. Association for Computational Linguistics. doi: 10.18653/v1/2020.findings-emnlp.360. URL https://www.aclweb.org/ anthology/2020.findings-emnlp.360.

### XSum

Shashi Narayan, Shay B. Cohen, and Mirella Lapata. Don’t give me the details, just the summary! topic-aware convolutional neural networks for extreme summarization. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1797–1807, Brussels, Belgium, October-November 2018. Association for Computational Linguistics. doi: 10.18653/v1/ D18-1206. URL https://aclanthology.org/D18-1206.

## 多语言Multilinguality 评测数据集

### XLSum

### WMT22

Tom Kocmi, Rachel Bawden, Ondřej Bojar, Anton Dvorkovich, Christian Federmann, Mark Fishel, Thamme Gowda, Yvette Graham, Roman Grundkiewicz, Barry Haddow, Rebecca Knowles, Philipp Koehn, Christof Monz, Makoto Morishita, Masaaki Nagata, Toshiaki Nakazawa, Michal Novák, Martin Popel, and Maja Popović. Findings of the 2022 conference on machine translation (WMT22). In Proceedings of the Seventh Conference on Machine Translation (WMT), December 2022. URL https://aclanthology.org/2022.wmt-1.1.

### WMT23

Kocmi Tom, Eleftherios Avramidis, Rachel Bawden, Ondřej Bojar, Anton Dvorkovich, Christian Federmann, Mark Fishel, Markus Freitag, Thamme Gowda, Roman Grundkiewicz, et al. Findings of the 2023 conference on machine translation (wmt23): Llms are here but not quite there yet. In WMT23-Eighth Conference on Machine Translation, pages 198–216, 2023.

### FRMT

Parker Riley, Timothy Dozat, Jan A Botha, Xavier Garcia, Dan Garrette, Jason Riesa, Orhan Firat, and Noah Constant. Frmt: A benchmark for few-shot region-aware machine translation. Transactions of the Association for Computational Linguistics, 2023.

### WikiLingua

### TydiQA

### MGSM

Freda Shi, Mirac Suzgun, Markus Freitag, Xuezhi Wang, Suraj Srivats, Soroush Vosoughi, Hyung Won Chung, Yi Tay, Sebastian Ruder, Denny Zhou, et al. Language models are multilingual chain-of- thought reasoners. ICLR, 2023.

### MMLU

### NTREX

Christian Federmann, Tom Kocmi, and Ying Xin. NTREX-128 – news test references for MT evaluation of 128 languages. In Proceedings of the First Workshop on Scaling Up Multilingual Evaluation, pages 21–24, Online, nov 2022. Association for Computational Linguistics. URL https://aclanthology.org/2022.sumeval-1.4.

### FLORES-200

NLLB Team, Marta R. Costa-jussà, James Cross, Onur Çelebi, Maha Elbayad, Kenneth Heafield, Kevin Heffernan, Elahe Kalbassi, Janice Lam, Daniel Licht, Jean Maillard, Anna Sun, Skyler Wang, Guillaume Wenzek, Al Youngblood, Bapi Akula, Loic Barrault, Gabriel Mejia Gonzalez, Prangthip Hansanti, John Hoffman, Semarley Jarrett, Kaushik Ram Sadagopan, Dirk Rowe, Shannon Spruit, Chau Tran, Pierre Andrews, Necip Fazil Ayan, Shruti Bhosale, Sergey Edunov, Angela Fan, Cynthia Gao, Vedanuj Goswami, Francisco Guzmán, Philipp Koehn, Alexandre Mourachko, Christophe Ropers, Safiyyah Saleem, Holger Schwenk, and Jeff Wang. No language left behind: Scaling human-centered machine translation. 2022.

## 图像理解Image Understanding评测数据集

### MMMU

Xiang Yue, Yuansheng Ni, Kai Zhang, Tianyu Zheng, Ruoqi Liu, Ge Zhang, Samuel Stevens, Dongfu Jiang, Weiming Ren, Yuxuan Sun, Cong Wei, Botao Yu, Ruibin Yuan, Renliang Sun, Ming Yin, Boyuan Zheng, Zhenzhu Yang, Yibo Liu, Wenhao Huang, Huan Sun, Yu Su, and Wenhu Chen. Mmmu: A massive multi-discipline multimodal understanding and reasoning benchmark for expert agi, 2023.

### TextVQA

Amanpreet Singh, Vivek Natarajan, Meet Shah, Yu Jiang, Xinlei Chen, Dhruv Batra, Devi Parikh, and Marcus Rohrbach. Towards VQA models that can read. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8317–8326, 2019.

### DocVQA

Minesh Mathew, Dimosthenis Karatzas, and CV Jawahar. Docvqa: A dataset for vqa on document images. In Proceedings of the IEEE/CVF winter conference on applications of computer vision, pages 2200–2209, 2021.

### ChartQA

Ahmed Masry, Do Long, Jia Qing Tan, Shafiq Joty, and Enamul Hoque. ChartQA: A benchmark for question answering about charts with visual and logical reasoning. In Findings of ACL, 2022.

### InfographicVQA

Minesh Mathew, Viraj Bagal, Rubèn Tito, Dimosthenis Karatzas, Ernest Valveny, and CV Jawahar. Infographicvqa. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 1697–1706, 2022.

### MathVista

Pan Lu, Hritik Bansal, Tony Xia, Jiacheng Liu, Chunyuan Li, Hannaneh Hajishirzi, Hao Cheng, Kai- Wei Chang, Michel Galley, and Jianfeng Gao. Mathvista: Evaluating mathematical reasoning of foundation models in visual contexts. arXiv preprint arXiv:2310.02255, 2023.

### AI2D

Aniruddha Kembhavi, Mike Salvato, Eric Kolve, Minjoon Seo, Hannaneh Hajishirzi, and Ali Farhadi. A diagram is worth a dozen images. In ECCV, 2016.

### VQAv2

Yash Goyal, Tejas Khot, Douglas Summers-Stay, Dhruv Batra, and Devi Parikh. Making the V in VQA matter: Elevating the role of image understanding in visual question answering. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 6904–6913, 2017.

### XM3600

Ashish V. Thapliyal, Jordi Pont-Tuset, Xi Chen, and Radu Soricut. Crossmodal-3600: A massively multilingual multimodal evaluation dataset. In EMNLP, 2022.

### Memecap

Hwang, EunJeong, and Vered Shwartz. MemeCap: A Dataset for Captioning and Interpreting Memes. arXiv preprint arXiv:2305.13703 (2023).

## 视频理解video understanding评测数据集

### VATEX

Xin Wang, Jiawei Wu, Junkun Chen, Lei Li, Yuan-Fang Wang, and William Yang Wang. VATEX: A large-scale, high-quality multilingual dataset for video-and-language research. In ICCV, 2019.

### YouCook2

Luowei Zhou, Chenliang Xu, and Jason J Corso. Towards automatic learning of procedures from web instructional videos. In AAAI Conference on Artificial Intelligence, pages 7590–7598, 2018.

### NextQA

Junbin Xiao, Xindi Shang, Angela Yao, and Tat-Seng Chua. NExT-QA: Next phase of question-answering to explaining temporal actions. In CVPR, 2021.

### ActivityNet-QA

Zhou Yu, Dejing Xu, Jun Yu, Ting Yu, Zhou Zhao, Yueting Zhuang, and Dacheng Tao. ActivityNet-QA: A dataset for understanding complex web videos via question answering. In AAAI, 2019.

### Perception Test MCQA

Viorica Pătrăucean, Lucas Smaira, Ankush Gupta, Adrià Recasens Continente, Larisa Markeeva, Dylan Banarse, Skanda Koppula, Joseph Heyward, Mateusz Malinowski, Yi Yang, et al. Perception test: A diagnostic benchmark for multimodal video models. arXiv preprint arXiv:2305.13786, 2023.

## 音频Audio评测数据集

### FLEURS

Alexis Conneau, Min Ma, Simran Khanuja, Yu Zhang, Vera Axelrod, Siddharth Dalmia, Jason Riesa, Clara Rivera, and Ankur Bapna. Fleurs: Few-shot learning evaluation of universal representations of speech. In 2022 IEEE Spoken Language Technology Workshop (SLT), pages 798–805. IEEE, 2023.

### VoxPopul

Changhan Wang, Morgane Riviere, Ann Lee, Anne Wu, Chaitanya Talnikar, Daniel Haziza, Mary Williamson, Juan Pino, and Emmanuel Dupoux. Voxpopuli: A large-scale multilingual speech corpus for representation learning, semi-supervised learning and interpretation. arXiv preprint arXiv:2101.00390, 2021.

### Librispeech

Vassil Panayotov, Guoguo Chen, Daniel Povey, and Sanjeev Khudanpur. Librispeech: an asr corpus based on public domain audio books. In 2015 IEEE international conference on acoustics, speech and signal processing (ICASSP), pages 5206–5210. IEEE, 2015.
### CoVoST 2

Changhan Wang, Anne Wu, and Juan Pino. Covost 2 and massively multilingual speech-to-text translation. arXiv preprint arXiv:2007.10310, 2020.

## 编码Coding评测数据集

### MBPP

Austin, Jacob, Augustus Odena, Maxwell Nye, Maarten Bosma, Henryk Michalewski, David Dohan, Ellen Jiang et al. "Program synthesis with large language models." arXiv preprint arXiv:2108.07732 (2021).

### CodeXGLUE

Lu, Shuai, Daya Guo, Shuo Ren, Junjie Huang, Alexey Svyatkovskiy, Ambrosio Blanco, Colin Clement et al. "Codexglue: A machine learning benchmark dataset for code understanding and generation." arXiv preprint arXiv:2102.04664 (2021).

### HumanEval

Chen, Mark, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards et al. "Evaluating large language models trained on code." arXiv preprint arXiv:2107.03374 (2021).

### CodeContests

Li, Yujia, David Choi, Junyoung Chung, Nate Kushman, Julian Schrittwieser, Rémi Leblond, Tom Eccles et al. "Competition-level code generation with alphacode." Science 378, no. 6624 (2022): 1092-1097.

### APPS

Hendrycks, Dan, Steven Basart, Saurav Kadavath, Mantas Mazeika, Akul Arora, Ethan Guo, Collin Burns et al. "Measuring coding challenge competence with apps." arXiv preprint arXiv:2105.09938 (2021).

### DeepFix

Gupta, Rahul, Soham Pal, Aditya Kanade, and Shirish Shevade. "Deepfix: Fixing common c language errors by deep learning." In Proceedings of the aaai conference on artificial intelligence, vol. 31, no. 1. 2017.

# 微信公众号：走向未来

欢迎扫码关注微信公众：**走向未来**，公众号专注于分享AGI、大模型、知识图谱、深度学习、强化学习等技术、系统架构、应用场景和案例等内容。

![](imgs/the-land-of-future.jpeg)

# 珠峰书

珠峰书《知识图谱：认知智能理论与实战》一书全面介绍了知识图谱的构建技术、存储技术和应用技术、Transformer、图神经网络等内容，欢迎购买。具体来说，包括：
- 知识图谱模式设计、知识抽取、图数据库、知识计算、知识推理、知识问答、知识推荐等全方面的内容
- 详细介绍了Transformer模型细节和实现方法，是大模型的基础技术
- 国内首本提到向量数据库的书籍
- 简要介绍了多模态知识融合的内容，书中以“月亮”为例，提出应当把图片的月亮、各种不同语言的文本月亮和和月亮的读音等，都应当融合到同一个知识点中。这正是多模态大模型所做的。
- 其他一些关于神经科学、脑科学和哲学中对智能的思考

![](imgs/kgbook.jpg)