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        <title>Designing Informative Metrics for Few-Shot Example Selection</title>
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    <abstract>Pretrained language models (PLMs) have shown remarkable few-shot learning capabilities when provided with properly formatted examples. However, selecting the “best” examples remains an open challenge. We propose a complexity-based prompt selection approach for sequence tagging tasks. This approach avoids the training of a dedicated model for selection of examples, and instead uses certain metrics to align the syntactico-semantic complexity of test sentences and examples. We use both sentence- and word-level metrics to match the complexity of examples to the (test) sentence being considered. Our results demonstrate that our approach extracts greater performance from PLMs: it achieves state-of-the-art performance on few-shot NER, achieving a 5% absolute improvement in F1 score on the CoNLL2003 dataset for GPT-4. We also see large gains of upto 28.85 points (F1/Acc.) in smaller models like GPT-j-6B.</abstract>
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%0 Conference Proceedings
%T Designing Informative Metrics for Few-Shot Example Selection
%A Adiga, Rishabh
%A Subramanian, Lakshmi
%A Chandrasekaran, Varun
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and virtual meeting
%F adiga-etal-2024-designing
%X Pretrained language models (PLMs) have shown remarkable few-shot learning capabilities when provided with properly formatted examples. However, selecting the “best” examples remains an open challenge. We propose a complexity-based prompt selection approach for sequence tagging tasks. This approach avoids the training of a dedicated model for selection of examples, and instead uses certain metrics to align the syntactico-semantic complexity of test sentences and examples. We use both sentence- and word-level metrics to match the complexity of examples to the (test) sentence being considered. Our results demonstrate that our approach extracts greater performance from PLMs: it achieves state-of-the-art performance on few-shot NER, achieving a 5% absolute improvement in F1 score on the CoNLL2003 dataset for GPT-4. We also see large gains of upto 28.85 points (F1/Acc.) in smaller models like GPT-j-6B.
%U https://aclanthology.org/2024.findings-acl.602
%P 10127-10135

Markdown (Informal)

[Designing Informative Metrics for Few-Shot Example Selection](https://aclanthology.org/2024.findings-acl.602) (Adiga et al., Findings 2024)

• Designing Informative Metrics for Few-Shot Example Selection (Adiga et al., Findings 2024)

ACL

• Rishabh Adiga, Lakshmi Subramanian, and Varun Chandrasekaran. 2024. Designing Informative Metrics for Few-Shot Example Selection. In Findings of the Association for Computational Linguistics ACL 2024, pages 10127–10135, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.