Researchers Propose Knowledge Distillation Method for Black‑Box Large Language Models

A paper posted on arXiv introduces a method for distilling knowledge from black‑box large language models. The approach aims to replicate model behavior without requiring access to the underlying weights or architecture. Authors describe a training pipeline that uses the target model’s outputs as supervision signals. The technique leverages publicly available prompts to query the black‑box model. Experiments evaluate how closely the distilled model matches the original on benchmark tasks. Results show the distilled model can achieve comparable performance on several metrics. The authors discuss potential applications such as model compression and privacy‑preserving deployment. They also note limitations related to query costs and fidelity of the distilled representation.