Researchers Propose Method to Distill Knowledge from Black-Box LLMs

A research paper published on arXiv explores techniques for knowledge distillation applied to large language models that operate as black boxes. Knowledge distillation involves transferring capabilities from a larger, more complex model into a smaller, more efficient one. The challenge with black-box LLMs is that their internal weights and architecture are inaccessible, making standard distillation methods difficult to apply. The study proposes approaches to work around these limitations using only model outputs. The paper was shared on Hacker News, where it received minimal engagement at the time of indexing.