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Self-Instruct: Aligning Language Models with Self-Generated Instructions
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Yizhong Wang, Yeganeh Kordi, Swaroop Mishra, Alisa Liu, Noah A. Smith, Daniel Khashabi, Hannaneh Hajishirzi. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.
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LLM-powered Data Augmentation for Enhanced Cross-lingual Performance
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An HCAI Methodological Framework: Putting it Into Action to Enable Human-Centered AI
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Evaluating Correctness and Faithfulness of Instruction-Following Models for Question Answering
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How to Bridge the Gap Between Modalities: Survey on Multimodal Large Language Model
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Metadata
- Authors
- Wang, Y., Kordi, Y., Mishra, S., Liu, A., Smith, N. A., Khashabi, D., & Hajishirzi, H.
- Affiliations
- Johns Hopkins University (US)
- Corresponding author
- Wang, Yizhong
- Venue
- Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2023
- Pages
- 13484-13508
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W4385572634
- Cited by
- 1,243
- References
- 39
- Citations by year
- 2026: 317 · 2025: 435 · 2024: 345 · 2023: 145
- FWCI
- 196.73
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Funders
- Defense Advanced Research Projects Agency (N66001-19-2-4031, N66001-19)
- Multidisciplinary University Research Initiative
- Office of Naval Research (N00014‐18‐1‐2670, N00014-18-1-2826, MURI N00014-18-1-2670, N00014)
- Naval Information Warfare Center Pacific
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Keywords
- self-education, language model alignment, instruction tuning, self-supervised learning, large language models
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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