MarginSel : Max-Margin Demonstration Selection for LLMs
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915332952686592 |
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| author | Ambati, Rajeev Bhatt Lester, James Srivastava, Shashank Chaturvedi, Snigdha |
| author_facet | Ambati, Rajeev Bhatt Lester, James Srivastava, Shashank Chaturvedi, Snigdha |
| contents | Large Language Models (LLMs) excel at few-shot learning via in-context learning (ICL). However, the effectiveness of ICL is often sensitive to the selection and ordering of demonstration examples. To address this, we present MarginSel: Max-Margin Demonstration Selection for LLMs, a two-step method that selects hard demonstration examples for the ICL prompt, adapting to each test instance. Our approach achieves 2-7% absolute improvement in F1-score across classification tasks, compared to a random selection of examples. We also provide theoretical insights and empirical evidence showing that MarginSel induces max-margin behavior in LLMs by effectively increasing the margin for hard examples, analogous to support vectors, thereby shifting the decision boundary in a beneficial direction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_06699 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MarginSel : Max-Margin Demonstration Selection for LLMs Ambati, Rajeev Bhatt Lester, James Srivastava, Shashank Chaturvedi, Snigdha Machine Learning Artificial Intelligence Computation and Language Large Language Models (LLMs) excel at few-shot learning via in-context learning (ICL). However, the effectiveness of ICL is often sensitive to the selection and ordering of demonstration examples. To address this, we present MarginSel: Max-Margin Demonstration Selection for LLMs, a two-step method that selects hard demonstration examples for the ICL prompt, adapting to each test instance. Our approach achieves 2-7% absolute improvement in F1-score across classification tasks, compared to a random selection of examples. We also provide theoretical insights and empirical evidence showing that MarginSel induces max-margin behavior in LLMs by effectively increasing the margin for hard examples, analogous to support vectors, thereby shifting the decision boundary in a beneficial direction. |
| title | MarginSel : Max-Margin Demonstration Selection for LLMs |
| topic | Machine Learning Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2506.06699 |