Want Better Synthetic Data? Steer It: Activation Steering for Low-Resource Language Generation
Quick Answer
This study introduces activation steering for generating synthetic data in low-resource languages, enhancing diversity and downstream performance.
Quick Take
Evaluating four open-source , the authors find that early-layer steering improves sentiment and topic classification tasks, outperforming traditional few-shot prompting methods.
Key Points
- Activation steering improves synthetic data generation for low-resource languages.
- Two strategies: Language Steering for linguistic identity and Quality Steering for well-formedness.
- Evaluated on four open-source LLMs across 11 diverse languages.
- Early-layer steering enhances data diversity and downstream model performance.
- Results show significant improvements in sentiment and topic classification tasks.
Paper Resources
Source Excerpt
(LLMs) have become an effective tool for synthetic data generation, including for low-resource languages, where generated data can improve downstream task performance. Current best-performing approaches typically rely on few-shot prompting with target-language examples, which increases inference costs and may reduce diversity through lexical anchoring. In this work, we investigate activation steering as an alternative for low-resource synthetic data generation. We study two
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