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Automatic Combination of Sample Selection Strategies for Few-Shot Learning

Paper · 2026 · Branislav Pecher, Ivan Srba, Maria Bielikova et al.

Summary

Picking which labelled examples to use strongly affects few-shot performance. Methods for large language models tend to rely on selectors built for specific in-context learning setups and overlook classic strategies from supervised learning, such as active learning and core-set methods. The authors propose ACSESS, which works in three stages:

  1. It defines a pool of single-property selection strategies covering informativeness, representativeness and learnability.
  2. It identifies the subset of those strategies that actually helps.
  3. It merges their normalised per-sample scores in a weighted sum and keeps the top-N samples.

The evaluation covers 23 strategies, 5 in-context learning models, and 3 gradient-based approaches (meta-learning and few-shot fine-tuning), on 6 text and 8 image datasets. The combined selection beats every individual strategy and matches or surpasses selectors tailored to in-context learning. Selection helps most with few shots or noisy data, and its advantage fades as more shots are added. This suggests that well-established, general-purpose criteria, when combined, remain a strong and portable option across very different few-shot paradigms.

Key points

  • Models tested: the in-context learning models are Mistral-7B, Zephyr-7B, LLaMA-3.1-8B, Gemma-3-4B and Qwen-2.5-7B. Image datasets include collections from MetaAlbum.
  • Gains from selection: up to about 5 percentage points for in-context learning and about 3 for gradient few-shot learning, with results varying strongly by dataset and approach.
  • Effect of shot count: around 30–40 shots, all strategies perform roughly like random selection. Adding more shots stops helping beyond about 30 for in-context learning and about 50 for gradient methods.
  • Small data pools: selecting from only 25% of the data (in-context learning) or 10% (gradient methods) yields performance similar to selecting from the full dataset.
  • Code and compute: code is released at github.com/kinit-sk/ACSESS. Experiments took roughly 2,500 A100 GPU-hours, estimated at about 270 kgCO2eq.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Brno University of TechnologyBrno · Czechia11 works · 32,326 citations🎓Eindhoven University of TechnologyEindhoven · Netherlands8 works · 602 citations🤝Kempelen Institute of Intelligent TechnologiesBratislava · Slovakia11 works · 82 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 works

Cite

Cite as Branislav Pecher, Ivan Srba, Maria Bielikova and Joaquin Vanschoren. 2026. Automatic Combination of Sample Selection Strategies for Few-Shot Learning. Findings of the Association for Computational Linguistics: ACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.findings-acl.2008

@inproceedings{pecher2026automatic,
  title = {{Automatic Combination of Sample Selection Strategies for Few-Shot Learning}},
  author = {Branislav Pecher, Ivan Srba, Maria Bielikova and Joaquin Vanschoren},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.2008},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.2008}
}

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