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MetaICL: Learning to Learn In Context
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Sewon Min, Mike Lewis, Luke Zettlemoyer, Hannaneh Hajishirzi. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2022.
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“Finetuning large language models (LLMs) on a variety of language tasks explained via instructions (instruction tuning) has been shown to improve model usability and general performance (Wei et al., 2022;Sanh et al., 2022;Min et al., 2022;Ouyang et al., 2022).”
From OctoPack: Instruction Tuning Code Large Language Models (2023) · cites this paper as Min et al., 2022
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Cited by
Sorted by citations
Finetuned Language Models Are Zero-Shot Learners
Wei, J., Bosma, M., Zhao, V. Y., Guu, K., Yu, A. W., Lester, B., Du, N., Dai, A. M., & Le, Q. V. (2021) · arXiv · doi:10.48550/arxiv.2109.01652 · Cited by 1,101
Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?
Min, S., Lyu, X., Holtzman, A., Artetxe, M., Lewis, M., Hajishirzi, H., & Zettlemoyer, L. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2022.emnlp-main.759 · Cited by 755
Self-citationA Survey on In-context Learning
Dong, Q., Li, L., Dai, D., Zheng, C., Ma, J., Li, R., Xia, H., Xu, J., Wu, Z., Chang, B., et al. (2024) · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2024.emnlp-main.64 · Cited by 649
QLoRA: Efficient Finetuning of Quantized LLMs
Dettmers, T., Pagnoni, A., Holtzman, A., & Zettlemoyer, L. (2023) · arXiv · doi:10.48550/arxiv.2305.14314 · Cited by 3,832
“Approaches and datasets include MetaICL [40], MetaTuning [74], InstructGPT [43], FLAN [63, 12], PromptSource [3], Super-NaturalInstructions [62, 51], Self-instruct [60], UnnaturalInstructions [26], OPT-IML [29], UnifiedSKG[68], OIG/Chip2 [32], Alpaca [56], Vicuna [10], Koala [20], and…”
BackgroundMethodSelf-citationPrecise Zero-Shot Dense Retrieval without Relevance Labels
Gao, L., Ma, X., Lin, J., & Callan, J. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2023.acl-long.99 · Cited by 362
Metadata
- Authors
- Min, S., Lewis, M., Zettlemoyer, L., & Hajishirzi, H.
- Affiliations
- University of Washington (US)
- Meta (United States) (US)
- Venue
- Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
- Published
- 2022
- Pages
- 2791-2809
- OpenAlex
- W3210277894
- Cited by
- 169
- References
- 137
- Citations by year
- 2026: 23 · 2025: 61 · 2024: 44 · 2023: 36 · 2022: 4
- FWCI
- 20.04
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) (publishedVersion, cc-by)
- Funders
- National Science Foundation (2044660)
- Office of Naval Research (N00014-18-1-2826, N00014)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- in-context learning, prompt engineering, large language models, meta-learning
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