Automatic Combination of Sample Selection Strategies for Few-Shot Learning (2026.findings-acl)
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| Challenge: | Existing studies on small language models are characterised by a labelled data scarcity due to data collection/annotation costs or privacy considerations, making the training of typical deep learning models unfeasible. |
| Approach: | They propose a method for Automatic Combination of SamplE Selection Strategies to leverage the strengths and complementarity of various well-established selection objectives. |
| Outcome: | The proposed method outperforms all in-context learning strategies and performs on par or exceeds the in-constinction learning specific baselines. |
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