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 modelgenerated 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 1 demonstrate the effectiveness of SALT in improving the summary quality with Human and Imitation Edits.Through additional experiments, we show that SALT outperforms the conventional RLHF method (designed for human preferences) -DPO, when applied to human-edit data.We hope the evidence in our paper prompts researchers to explore, collect and better use different human feedback approaches scalably.

With a growing number of robots performing autonomously without human intervention, it is difficult to understand what the robots experience along their routes during execution without looking at execution logs. Rather than looking through logs, our goal is for robots to respond to queries in natural language about what they experience and what routes they have chosen. We propose verbalization as the process of converting route experiences into natural language, and highlight the importance of varying verbalizations based on user preferences. We present our verbalization space representing different dimensions that verbalizations can be varied, and our algorithm for automatically generating them on our CoBot robot. Then we present our study of how users can request different verbalizations in dialog. Using the study data, we learn a language model to map user dialog to the verbalization space. Finally, we demonstrate the use of the learned model within a dialog system in order for any user to request information about CoBot's route experience at varying levels of detail.