Impossible Distillation: from Low-Quality Model to High-Quality Dataset
  & Model for Summarization and Paraphrasing
Authors: name: Jaehun Jung, name: Peter West, name: Liwei Jiang, name: Faeze Brahman, name: Ximing Lu, name: Jillian Fisher, name: Taylor Sorensen, name: Yejin Choi

It is commonly perceived that the strongest language models (LMs) rely on a
combination of massive scale, instruction data, and human feedback to perform
specialized tasks -- e.g. summarization and paraphrasing, without supervision.
In this paper, we propose that language models can learn to summarize and
paraphrase sentences, with none of these 3 factors. We present Impossible
Distillation, a framework that distills a task-specific dataset directly from
an off-the-shelf LM, even when it is impossible for the LM itself to reliably
solve the task. By training a student model on the generated dataset and
amplifying its capability through self-distillation, our method yields a
high-quality model and dataset from a low-quality teacher model, without the
need for scale or supervision. Using Impossible Distillation, we are able to
distill an order of magnitude smaller model (with only 770M parameters) that
outperforms 175B parameter GPT-3, in both quality and controllability, as
confirmed by automatic and human evaluations. Furthermore, as a useful
byproduct of our approach, we obtain DIMSUM+, a high-quality dataset with 3.4M
sentence summaries and paraphrases. Our analyses show that this dataset, as a
purely LM-generated corpus, is more diverse and more effective for
generalization to unseen domains than all human-authored datasets -- including
Gigaword with 4M samples.