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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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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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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