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Large language models (LLMs) have achieved impressive performance across various natural language benchmarks, prompting a continual need to curate more difficult datasets for larger LLMs, which is costly and time-consuming.
A revision of bloom’s taxonomy: An overview
D. R. Krathwohl · 2002
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Meteor: An automatic metric for mt evaluation with improved correlation with human judgments
S. Banerjee and A. Lavie · 2005
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Squad: 100,000+ questions for machine comprehension of text
P. Rajpurkar, J. Zhang, K. Lopyrev, and P. Liang · 2016
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Think you have solved question answering? try arc, the ai2 reasoning challenge
P. Clark, I. Cowhey, O. Etzioni, T. Khot, A. Sabharwal, C. Schoenick, and O. Tafjord · 2018
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Glue: A multi-task benchmark and analysis platform for natural language understanding
A. Wang, A. Singh, J. Michael, F. Hill, O. Levy, and S. R. Bowman · 2018
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Coqa: A conversational question answering challenge
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Commonsenseqa: A question answering challenge targeting commonsense knowledge
A. Talmor, J. Herzig, N. Lourie, and J. Berant · 2019
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Hellaswag: Can a machine really finish your sentence?
R. Zellers, A. Holtzman, Y. Bisk, A. Farhadi, and Y. Choi · 2019
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Language models are few-shot learners
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Documenting large webtext corpora: A case study on the colossal clean crawled corpus
J. Dodge, M. Sap, A. Marasović, W. Agnew, G. Ilharco, D. Groeneveld, M. Mitchell, and M. Gardner · 2021
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Measuring massive multitask language understanding
D. Hendrycks, C. Burns, S. Basart, A. Zou, M. Mazeika, D. Song, and J. Steinhardt · 2021
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Lora: Low-rank adaptation of large language models
E. J. Hu, Y. Shen, P. Wallis, Z. Allen-Zhu, Y. Li, S. Wang, L. Wang, and W. Chen · 2021
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Winogrande: An adversarial winograd schema challenge at scale
K. Sakaguchi, R. L. Bras, C. Bhagavatula, and Y. Choi · 2021
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Multitask prompted training enables zero-shot task generalization, 2021
V. Sanh, A. Webson, C. Raffel, S. H. Bach, L. Sutawika, Z. Alyafeai, A. Chaffin, A. Stiegler, T. L. Scao, A. Raja, M. Dey, M. S. Bari, C. Xu, U. Thakker, S. S. Sharma, E. Szczechla, T. Kim, G. Chhablani, N. Nayak, D. Datta, J. Chang, M. T.-J. Jiang, H. Wang, M. Manica, S. Shen, Z. X. Yong, H. Pandey, R. Bawden, T. Wang, T. Neeraj, J. Rozen, A. Sharma, A. Santilli, T. Fevry, J. A. Fries, R. Teehan, S. Biderman, L. Gao, T. Bers, T. Wolf, and A. M. Rush · 2021
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Finetuned language models are zero-shot learners
J. Wei, M. Bosma, V. Y. Zhao, K. Guu, A. W. Yu, B. Lester, N. Du, A. M. Dai, and Q. V. Le · 2021
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Glam: Efficient scaling of language models with mixture-of-experts
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Introducing chatgpt, 2022
OpenAI · 2022
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Claude 2.1, 2023
Anthropic · 2023
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Benchmarking foundation models with language-model-as-an-examiner
Y. Bai, J. Ying, Y. Cao, X. Lv, Y. He, X. Wang, J. Yu, K. Zeng, Y. Xiao, H. Lyu, et al · 2023
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Beyond the imitation game: Quantifying and extrapolating the capabilities of language models
Openai: Gpt-4, 2023
OpenAI · 2023
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Pretraining on the test set is all you need
R. Schaeffer · 2023
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Detecting pretraining data from large language models
W. Shi, A. Ajith, M. Xia, Y. Huang, D. Liu, T. Blevins, D. Chen, and L. Zettlemoyer · 2023
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Skywork: A more open bilingual foundation model
T. Wei, L. Zhao, L. Zhang, B. Zhu, L. Wang, H. Yang, B. Li, C. Cheng, W. Lü, R. Hu, et al · 2023
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Lamini-lm: A diverse herd of distilled models from large-scale instructions
M. Wu, A. Waheed, C. Zhang, M. Abdul-Mageed, and A. F. Aji · 2023
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B. bench authors · 2023
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Gemini, 2023
Google · 2023
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A. Q. Jiang, A. Sablayrolles, A. Mensch, C. Bamford, D. S. Chaplot, D. d. l. Casas, F. Bressand, G. Lengyel, G. Lample, L. Saulnier, et al · 2023
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An open source data contamination report for llama series models
Y. Li · 2023
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Yi, 2023
lingyiwanwu · 2023
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The flan collection: Designing data and methods for effective instruction tuning, 2023
S. Longpre, L. Hou, T. Vu, A. Webson, H. W. Chung, Y. Tay, D. Zhou, Q. V. Le, B. Zoph, J. Wei, and A. Roberts · 2023
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When not to trust language models: Investigating effectiveness of parametric and non-parametric memories
A. Mallen, A. Asai, V. Zhong, R. Das, D. Khashabi, and H. Hajishirzi · 2023
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Wizardlm: Empowering large language models to follow complex instructions, 2023
C. Xu, Q. Sun, K. Zheng, X. Geng, P. Zhao, J. Feng, C. Tao, and D. Jiang · 2023
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Kola: Carefully benchmarking world knowledge of large language models
J. Yu, X. Wang, S. Tu, S. Cao, D. Zhang-Li, X. Lv, H. Peng, Z. Yao, X. Zhang, H. Li, et al · 2023
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Judging llm-as-a-judge with mt-bench and chatbot arena
L. Zheng, W.-L. Chiang, Y. Sheng, S. Zhuang, Z. Wu, Y. Zhuang, Z. Lin, Z. Li, D. Li, E. Xing, et al · 2023
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Don’t make your llm an evaluation benchmark cheater
K. Zhou, Y. Zhu, Z. Chen, W. Chen, W. X. Zhao, X. Chen, Y. Lin, J.-R. Wen, and J. Han · 2023
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Anthropic: Claude-3, 2024
Anthropic · 2024
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Mixtral of experts, 2024
A. Q. Jiang, A. Sablayrolles, A. Roux, A. Mensch, B. Savary, C. Bamford, D. S. Chaplot, D. de las Casas, E. B. Hanna, F. Bressand, G. Lengyel, G. Bour, G. Lample, L. R. Lavaud, L. Saulnier, M.-A. Lachaux, P. Stock, S. Subramanian, S. Yang, S. Antoniak, T. L. Scao, T. Gervet, T. Lavril, T. Wang, T. Lacroix, and W. E. Sayed · 2024
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Introducing meta llama 3: The most capable openly available llm to date, 2024
Meta · 2024
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Kieval: A knowledge-grounded interactive evaluation framework for large language models
Z. Yu, C. Gao, W. Yao, Y. Wang, W. Ye, J. Wang, X. Xie, Y. Zhang, and S. Zhang · 2024
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