EvoSelect: Data-Efficient LLM Evolution for Targeted Task Adaptation
Adapting large language models (LLMs) to a targeted task efficiently and effectively remains a fundamental challenge. Such adaptation often requires iteratively improving the model toward a targeted task, yet collecting high-quality human-labeled data to support this process is costly and difficult to scale. As a result, synthetic data generation has emerged as a flexible and scalable alternative. One straightforward approach is through an iterative generation-training loop, where candidate data are synthesized through an external generator, the model is updated using these data and the process is repeated over iterations. However, generated samples can be noisy, highly redundant, or even...
| Authors | Ting-Wei Li, Sirui Chen, Jiaru Zou et al. |
|---|---|
| Primary category | cs.CL |
| Published | 28 April 2026 |
| Last updated at source | 20 August 2026 |
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