Recent work has shown the promise of learning with human feedback paradigms
to produce human-determined high-quality text. Existing works use human
feedback to train large language models (LLMs) in general domain abstractive
summarization and have obtained summary quality exceeding traditional
likelihood training. In this paper, we focus on a less explored form of human
feedback -- Human Edits. We propose Sequence Alignment (un)Likelihood Training
(SALT), a novel technique to use both the human-edited and model-generated data
together in the training loop. In addition, we demonstrate simulating Human
Edits with ground truth summaries coming from existing training data --
Imitation edits, along with the model-generated summaries obtained after the
training, to reduce the need for expensive human-edit data. In our experiments,
we extend human feedback exploration from general domain summarization to
medical domain summarization. Our results demonstrate the effectiveness of SALT
to improve the summary quality with Human and Imitation Edits.

通过人类反馈范式学习的大型语言模型以及人类编辑和模型生成数据结合的新技术 Sequence Alignment (un) Likelihood Training (SALT) 在医学领域自动文摘中展示了有效性。