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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Instruction tuning of large language models on natural language tasks improves their usability and general performance.
“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
The paper’s instruction ablation reports that multi-task zero-shot models outperform a raw-LM baseline and that adding instructions improves MetaICL’s task performance (section “Number of meta-training tasks”; page unavailable). It does not establish the claimed improvement in model usability, and its strongest performance finding concerns combining instructions with MetaICL rather than instruction tuning alone. (LLM, conf=0.83)
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Large Language Models Are Neurosymbolic Reasoners
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LLM-Assisted Multi-Teacher Continual Learning for Visual Question Answering in Robotic Surgery
Chen, K., Du, Y., You, T., Islam, M., Guo, Z., Jin, Y., Chen, G., & Heng, P. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10610603 · Cited by 21 · see citing works
Tc-llama 2: fine-tuning LLM for technology and commercialization applications
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Repeatability of Fine-Tuning Large Language Models Illustrated Using QLoRA
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The role of digital technology tools usage on organisational performance continuity
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OctoPack: Instruction Tuning Code Large Language Models
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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).”
BackgroundInstruct Me More! Random Prompting for Visual In-Context Learning
Zhang, J., Wang, B.-W., Li, L.-Z., Nakashima, Y., & Nagahara, H. (2023) · IEEE Workshop/Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00258 · Cited by 42 · see citing works
Beyond Text: Frozen Large Language Models in Visual Signal Comprehension
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Automatic Lesson Plan Generation via Large Language Models with Self-critique Prompting
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Meta-prompting for Automating Zero-Shot Visual Recognition with LLMs
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Contrastive classification: A label-independent generalization model for text classification
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PaRaDe: Passage Ranking using Demonstrations with LLMs
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LongLLMLingua: Accelerating and Enhancing LLMs in Long Context Scenarios via Prompt Compression
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“The commonly used technologies such as In-Context Learning (ICL) (Min et al., 2022; Dong et al., 2023), Retrieval Augment Generation (RAG) (Lewis et al., 2020; Asai et al., 2024), and Multi-turn Agent (Shen et al., 2024; Park et al., 2023; Wu et al., 2023a) are driving prompts to be increasingly…”
MethodThe Impact of LLM Hallucinations on Motor Skill Learning: A Case Study in Badminton
Qiu, Y. (2024) · IEEE Access · doi:10.1109/access.2024.3444783 · Cited by 16 · see citing works
Contextual Emotion Recognition using Large Vision Language Models
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Context-aware prompt learning for test-time vision recognition with frozen vision-language model
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Playing the Fool: Jailbreaking LLMs and Multimodal LLMs with Out-of-Distribution Strategy
Jeong, J., Bae, S., Jung, Y., Hwang, J., & Yang, E. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.02786 · Cited by 12 · see citing works
Transformers learn to implement preconditioned gradient descent for in-context learning
Ahn, K., Cheng, X., Daneshmand, H., & Sra, S. (2023) · arXiv · doi:10.48550/arxiv.2306.00297 · Cited by 11 · see citing works
“…property has become the focus of body of recent research that aims to shed light on the underlying mechanism of large language models (Garg et al., 2022; Akyürek et al., 2022; von Oswald et al., 2023; Li and Malik, 2017; Min et al., 2021; Xie et al., 2021; Elhage et al., 2021; Olsson et al., 2022).”
BackgroundLi, S., Chen, J., Yuan, S., Wu, X., Yang, H., Tao, S., & Xiao, Y. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i17.29817 · Cited by 21 · see citing works
In-Context Learning for MIMO Equalization Using Transformer-Based Sequence Models
Zecchin, M., Yu, K., & Simeone, O. (2024) · IEEE International Conference on Communications workshops · doi:10.1109/iccworkshops59551.2024.10615360 · Cited by 11 · see citing works
MAML-en-LLM: Model Agnostic Meta-Training of LLMs for Improved In-Context Learning
Sinha, S., Yue, Y., Soto, V., Kulkarni, M., Lu, J., & Zhang, A.-D. (2024) · Knowledge Discovery and Data Mining · doi:10.1145/3637528.3671905 · Cited by 21 · see citing works
Towards More Unified In-Context Visual Understanding
Sheng, D.-M., Chen, D.-D., Tan, Z., Liu, Q., Chu, Q., Bao, J., Gong, T., Liu, B., Xu, S., & Yu, N.-H. (2023) · Computer Vision and Pattern Recognition · doi:10.1109/cvpr52733.2024.01269 · Cited by 19 · see citing works
Learning to Initialize: Can Meta Learning Improve Cross-task Generalization in Prompt Tuning?
Qin, C., Joty, S., Li, Q., & Zhao, R. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.659 · Cited by 12 · see citing works
Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research
Soldaini, L., Kinney, R., Bhagia, A., Schwenk, D., Atkinson, D., Authur, R., Bogin, B., Chandu, K., Dumas, J., Elazar, Y., et al. (2024) · arXiv · doi:10.48550/arxiv.2402.00159 · Cited by 10 · see citing works
Self-citationChallenges in moderating disruptive player behavior in online competitive action games
Kocielnik, R., Li, Z., Kann, C. K., Sambrano, D. C., Morrier, J., Linegar, M., Taylor, C., Kim, M. G., Naqvie, N., Soltani, F., et al. (2024) · Frontiers in Computer Science · doi:10.3389/fcomp.2024.1283735 · Cited by 10 · see citing works
Drilling Down into the Discourse Structure with LLMs for Long Document Question Answering
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Working Memory Capacity of ChatGPT: An Empirical Study
Gong, D., Wan, X., & Wang, D. (2023) · AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i9.28868 · Cited by 41 · see citing works
MetaVL: Transferring In-Context Learning Ability From Language Models to Vision-Language Models
Monajatipoor, M., Li, L. H., Rouhsedaghat, M., Yang, L. F., & Chang, K.-W. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-short.43 · Cited by 9 · see citing works
Empowering Vision-Language Models for Reasoning Ability through Large Language Models
Yang, Y., Zhang, X., Xu, J., & Han, W. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10446407 · Cited by 9 · see citing works
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Democratizing Reasoning Ability: Tailored Learning from Large Language Model
Wang, Z., Huang, S., Liu, Y., Wang, J., Song, M., Zhang, Z., Huang, H., Wei, F., Deng, W., Sun, F., et al. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.120 · Cited by 14 · see citing works
A Survey on the Recent Advancements in Human-Centered Dialog Systems
Oruche, R., Goruganthu, S. K., Akula, R., Cheng, X., Goni, M. A., Shibo, B. W., Kee, K. F., Zampieri, M., & Calyam, P. P. (2025) · ACM Computing Surveys · doi:10.1145/3729220 · Cited by 9 · see citing works
PCR-Chain: Partial Code Reuse Assisted by Hierarchical Chaining of Prompts on Frozen Copilot
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Towards Multimodal In-context Learning for Vision and Language Models
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A Zero-Training Error Correction System with Large Language Models
Wu, Y., Yang, C., Zhu, M., Miao, X., Ni, W., Xi, M., Zhao, X., & Yin, J. (2025) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde65448.2025.00221 · Cited by 7 · see citing works
Valuing Training Data via Causal Inference for In-Context Learning
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FiD-ICL: A Fusion-in-Decoder Approach for Efficient In-Context Learning
Ye, Q., Beltagy, I., Peters, M. E., Ren, X., & Hajishirzi, H. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.454 · Cited by 7 · see citing works
Self-citationBoosting Natural Language Generation from Instructions with Meta-Learning
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SINC: Self-Supervised In-Context Learning for Vision-Language Tasks
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ProtEx: A Retrieval-Augmented Approach for Protein Function PredictionPreprint
Shaw, P., Gurram, B., Belanger, D., Gane, A., Bileschi, M. L., Colwell, L. J., Toutanova, K., & Parikh, A. P. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.05.30.596539 · Cited by 8 · see citing works
ZeroED: Hybrid Zero-Shot Error Detection Through Large Language Model Reasoning
Ni, W., Zhang, K., Miao, X., Zhao, X., Wu, Y., Wang, Y., & Yin, J. (2025) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde65448.2025.00234 · Cited by 6 · see citing works
Mathematical Language Models: A Survey
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Fine-tuning Large Language Models with Limited Data: A Survey and Practical Guide
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IDAS: Intent Discovery with Abstractive Summarization
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Autonomous AI Agents for Multi-Platform Social Media Marketing: A Simultaneous Deployment Study
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GenKP: generative knowledge prompts for enhancing large language models
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Know Where You’re Going: Meta-Learning for Parameter-Efficient Fine-Tuning
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Compositional Zero-Shot Domain Transfer with Text-to-Text Models
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Contextual Interaction for Argument Post Quality Assessment
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- 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 · 2021: 1
- 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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