Papers with Self-Foveate
Self-Foveate: Enhancing Diversity and Difficulty of Synthesized Instructions from Unsupervised Text via Multi-Level Foveation (2025.findings-acl)
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| Challenge: | Existing methods for training large language models rely on human effort for data annotation. |
| Approach: | They propose an unsupervised method that generates unsupervised instruction from unsupervised text using a "Micro-Scatter-Macro" method that excavates fine-grained information embedded in unsupervised texts. |
| Outcome: | The proposed method improves diversity and difficulty of synthesized instructions across multiple unsupervised corpora and diverse model architectures. |