Affinity and Diversity: A Unified Metric for Demonstration Selection via Internal Representations
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866913700406886400 |
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| author | Kato, Mariko Cho, Hakaze Sakai, Yoshihiro Inoue, Naoya |
| author_facet | Kato, Mariko Cho, Hakaze Sakai, Yoshihiro Inoue, Naoya |
| contents | The performance of In-Context Learning (ICL) is highly sensitive to the selected demonstrations. Existing approaches to demonstration selection optimize different objectives, yielding inconsistent results. To address this, we propose a unified metric--affinity and diversity--that leverages ICL model's internal representations. Our experiments show that both affinity and diversity strongly correlate with test accuracies, indicating their effectiveness for demonstration selection. Moreover, we show that our proposed metrics align well with various previous works to unify the inconsistency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_14380 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Affinity and Diversity: A Unified Metric for Demonstration Selection via Internal Representations Kato, Mariko Cho, Hakaze Sakai, Yoshihiro Inoue, Naoya Computation and Language Artificial Intelligence Machine Learning The performance of In-Context Learning (ICL) is highly sensitive to the selected demonstrations. Existing approaches to demonstration selection optimize different objectives, yielding inconsistent results. To address this, we propose a unified metric--affinity and diversity--that leverages ICL model's internal representations. Our experiments show that both affinity and diversity strongly correlate with test accuracies, indicating their effectiveness for demonstration selection. Moreover, we show that our proposed metrics align well with various previous works to unify the inconsistency. |
| title | Affinity and Diversity: A Unified Metric for Demonstration Selection via Internal Representations |
| topic | Computation and Language Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2502.14380 |