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        <title>MEAL: Stable and Active Learning for Few-Shot Prompting</title>
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        <namePart type="given">Abdullatif</namePart>
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    <abstract>Few-shot classification has made great strides due to foundation models that, through priming and prompting, are highly effective few-shot learners. However, this approach has high variance both across different sets of few shots (*data selection*) and across different finetuning runs (*run variability*). This is problematic not only because it impedes the fair comparison of different approaches, but especially because it makes few-shot learning too unreliable for many real-world applications. To alleviate these issues, we make two contributions for more stable and effective few-shot learning: First, we propose novel ensembling methods and show that they substantially reduce *run variability*. Second, we introduce a new active learning (AL) criterion for *data selection* and present the first AL-based approach specifically tailored towards prompt-based learning. In our experiments, we show that our combined method, MEAL (**M**ultiprompt finetuning and prediction **E**nsembling with **A**ctive **L**earning), improves overall performance of prompt-based finetuning by 2.3 points on five diverse tasks. We publicly share our code and data splits in https://github.com/akoksal/MEAL.</abstract>
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    <identifier type="doi">10.18653/v1/2023.findings-emnlp.36</identifier>
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%0 Conference Proceedings
%T MEAL: Stable and Active Learning for Few-Shot Prompting
%A Köksal, Abdullatif
%A Schick, Timo
%A Schuetze, Hinrich
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F koksal-etal-2023-meal
%X Few-shot classification has made great strides due to foundation models that, through priming and prompting, are highly effective few-shot learners. However, this approach has high variance both across different sets of few shots (*data selection*) and across different finetuning runs (*run variability*). This is problematic not only because it impedes the fair comparison of different approaches, but especially because it makes few-shot learning too unreliable for many real-world applications. To alleviate these issues, we make two contributions for more stable and effective few-shot learning: First, we propose novel ensembling methods and show that they substantially reduce *run variability*. Second, we introduce a new active learning (AL) criterion for *data selection* and present the first AL-based approach specifically tailored towards prompt-based learning. In our experiments, we show that our combined method, MEAL (**M**ultiprompt finetuning and prediction **E**nsembling with **A**ctive **L**earning), improves overall performance of prompt-based finetuning by 2.3 points on five diverse tasks. We publicly share our code and data splits in https://github.com/akoksal/MEAL.
%R 10.18653/v1/2023.findings-emnlp.36
%U https://aclanthology.org/2023.findings-emnlp.36
%U https://doi.org/10.18653/v1/2023.findings-emnlp.36
%P 506-517

Markdown (Informal)

[MEAL: Stable and Active Learning for Few-Shot Prompting](https://aclanthology.org/2023.findings-emnlp.36) (Köksal et al., Findings 2023)

• MEAL: Stable and Active Learning for Few-Shot Prompting (Köksal et al., Findings 2023)

ACL

• Abdullatif Köksal, Timo Schick, and Hinrich Schuetze. 2023. MEAL: Stable and Active Learning for Few-Shot Prompting. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 506–517, Singapore. Association for Computational Linguistics.