@inproceedings{pecher-etal-2026-automatic,
title = "Automatic Combination of Sample Selection Strategies for Few-Shot Learning",
author = "Pecher, Branislav and
Srba, Ivan and
Bielikova, Maria and
Vanschoren, Joaquin",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.2008/",
pages = "40385--40416",
ISBN = "979-8-89176-395-1",
abstract = "In few-shot learning, the selection of samples has a significant impact on the performance of the model. While effective sample selection strategies are well-established in supervised settings, research on large language models largely overlooks them, favouring strategies specifically tailored to individual in-context learning settings. In this paper, we propose a new method for Automatic Combination of SamplE Selection Strategies (ACSESS) to leverage the strengths and complementarity of various well-established selection objectives. We investigate and compare the impact of 23 sample selection strategies on the performance of 5 in-context learning models and 3 few-shot learning approaches (meta-learning, few-shot fine-tuning) over 6 text and 8 image datasets. The experimental results show that the combination of strategies through the ACSESS method consistently outperforms all individual selection strategies and performs on par or exceeds the in-context learning specific baselines. Lastly, we demonstrate that sample selection remains effective even on smaller datasets, yielding the greatest benefits when only a few shots are selected, while its advantage diminishes as the number of shots increases."
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<abstract>In few-shot learning, the selection of samples has a significant impact on the performance of the model. While effective sample selection strategies are well-established in supervised settings, research on large language models largely overlooks them, favouring strategies specifically tailored to individual in-context learning settings. In this paper, we propose a new method for Automatic Combination of SamplE Selection Strategies (ACSESS) to leverage the strengths and complementarity of various well-established selection objectives. We investigate and compare the impact of 23 sample selection strategies on the performance of 5 in-context learning models and 3 few-shot learning approaches (meta-learning, few-shot fine-tuning) over 6 text and 8 image datasets. The experimental results show that the combination of strategies through the ACSESS method consistently outperforms all individual selection strategies and performs on par or exceeds the in-context learning specific baselines. Lastly, we demonstrate that sample selection remains effective even on smaller datasets, yielding the greatest benefits when only a few shots are selected, while its advantage diminishes as the number of shots increases.</abstract>
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%0 Conference Proceedings
%T Automatic Combination of Sample Selection Strategies for Few-Shot Learning
%A Pecher, Branislav
%A Srba, Ivan
%A Bielikova, Maria
%A Vanschoren, Joaquin
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F pecher-etal-2026-automatic
%X In few-shot learning, the selection of samples has a significant impact on the performance of the model. While effective sample selection strategies are well-established in supervised settings, research on large language models largely overlooks them, favouring strategies specifically tailored to individual in-context learning settings. In this paper, we propose a new method for Automatic Combination of SamplE Selection Strategies (ACSESS) to leverage the strengths and complementarity of various well-established selection objectives. We investigate and compare the impact of 23 sample selection strategies on the performance of 5 in-context learning models and 3 few-shot learning approaches (meta-learning, few-shot fine-tuning) over 6 text and 8 image datasets. The experimental results show that the combination of strategies through the ACSESS method consistently outperforms all individual selection strategies and performs on par or exceeds the in-context learning specific baselines. Lastly, we demonstrate that sample selection remains effective even on smaller datasets, yielding the greatest benefits when only a few shots are selected, while its advantage diminishes as the number of shots increases.
%U https://aclanthology.org/2026.findings-acl.2008/
%P 40385-40416
Markdown (Informal)
[Automatic Combination of Sample Selection Strategies for Few-Shot Learning](https://aclanthology.org/2026.findings-acl.2008/) (Pecher et al., Findings 2026)
ACL