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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2508.09544 |
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| _version_ | 1866911103583256576 |
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| author | Tavakkol, Sasan Chen, Lin Springer, Max Schantz, Abigail Bratanič, Blaž Cohen-Addad, Vincent Bateni, MohammadHossein |
| author_facet | Tavakkol, Sasan Chen, Lin Springer, Max Schantz, Abigail Bratanič, Blaž Cohen-Addad, Vincent Bateni, MohammadHossein |
| contents | Scarcity of labeled data, especially for rare events, hinders training effective machine learning models. This paper proposes SYNAPSE-G (Synthetic Augmentation for Positive Sampling via Expansion on Graphs), a novel pipeline leveraging Large Language Models (LLMs) to generate synthetic training data for rare event classification, addressing the cold-start problem. This synthetic data serve as seeds for semi-supervised label propagation on a similarity graph constructed between the seeds and a large unlabeled dataset. This identifies candidate positive examples, subsequently labeled by an oracle (human or LLM). The expanded dataset then trains/fine-tunes a classifier. We theoretically analyze how the quality (validity and diversity) of the synthetic data impacts the precision and recall of our method. Experiments on the imbalanced SST2 and MHS datasets demonstrate SYNAPSE-G's effectiveness in finding positive labels, outperforming baselines including nearest neighbor search. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_09544 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SYNAPSE-G: Bridging Large Language Models and Graph Learning for Rare Event Classification Tavakkol, Sasan Chen, Lin Springer, Max Schantz, Abigail Bratanič, Blaž Cohen-Addad, Vincent Bateni, MohammadHossein Machine Learning Scarcity of labeled data, especially for rare events, hinders training effective machine learning models. This paper proposes SYNAPSE-G (Synthetic Augmentation for Positive Sampling via Expansion on Graphs), a novel pipeline leveraging Large Language Models (LLMs) to generate synthetic training data for rare event classification, addressing the cold-start problem. This synthetic data serve as seeds for semi-supervised label propagation on a similarity graph constructed between the seeds and a large unlabeled dataset. This identifies candidate positive examples, subsequently labeled by an oracle (human or LLM). The expanded dataset then trains/fine-tunes a classifier. We theoretically analyze how the quality (validity and diversity) of the synthetic data impacts the precision and recall of our method. Experiments on the imbalanced SST2 and MHS datasets demonstrate SYNAPSE-G's effectiveness in finding positive labels, outperforming baselines including nearest neighbor search. |
| title | SYNAPSE-G: Bridging Large Language Models and Graph Learning for Rare Event Classification |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2508.09544 |